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@@ -56,3 +56,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+data_analysis/data/00000009/MO2017-Finals-Sec-3-Word-Play[[:space:]](1).xlsb filter=lfs diff=lfs merge=lfs -text

data_analysis/compute_answer.py

@@ -0,0 +1,120 @@
+import json
+
+from tqdm import tqdm
+import os
+from openai import OpenAI
+
+client = OpenAI(api_key="")
+
+samples = []
+with open("./data.json", "r") as f:
+    for line in f:
+        samples.append(eval(line.strip()))
+
+
+def evaluate_prediction(client, question, answer, prediction):
+    prompt = (f"Please judge whether the generated answer is right or wrong. We require that the correct answer "
+              f"to the prediction gives a clear answer, not just a calculation process or a disassembly of ideas. "
+              f"The question is {question}. The true answer is \n {answer}. \n The predicted answer is \n {prediction}.\n "
+              f"If the predicted answer is right, please output True. Otherwise output Flase. "
+              f"Don't output any other text content. You only can output True or False.")
+    response = client.chat.completions.create(
+      model="gpt-4o-2024-05-13",
+      messages=[
+        {
+          "role": "user",
+          "content": [
+            {
+              "type": "text",
+              "text": prompt
+            }
+          ]
+        }
+      ],
+      temperature=0,
+      max_tokens=256,
+      top_p=1,
+      frequency_penalty=0,
+      presence_penalty=0
+    )
+    # print(prompt)
+    # print(response.choices[0].message.content)
+    # exit()
+    return response.choices[0].message.content
+
+def read_txt(path):
+    with open(path, "r") as f:
+        return f.read()
+
+save_path = "./save_process"
+model = "gpt-3.5-turbo-0125"
+# model = 'gpt-4o-2024-05-13'
+# model = 'llama-3-8b-instruct'
+# model = 'gpt-3.5-turbo-0125-autoagent'
+# model = 'gpt-4o-2024-05-13-autoagent'
+# model = 'llava-v1.5-13b'
+# model = 'llama3-autoagent'
+
+results = []
+save_f = open(os.path.join(save_path, model, "results.json"), "w")
+save_process = open(os.path.join(save_path, model, "results_process.json"), "w")
+
+for sample in tqdm(samples):
+    result = []
+    if len(sample["questions"]) > 0:
+        # print(sample['id'])
+        predicts = []
+        with open(os.path.join(save_path, model, sample['id']+".json"), "r") as f:
+            for line in f:
+                predicts.append(eval(line.strip()))
+
+        questions = []
+        for id, question_name in enumerate(tqdm(sample["questions"])):
+            question = read_txt(os.path.join("./data", sample["id"], question_name + ".txt"))
+            pre = predicts[id]
+            try:
+                if not model.endswith('autoagent'):
+                    ans = evaluate_prediction(client, question, str(sample["answers"][id]), pre['response'])
+                else:
+                    ans = evaluate_prediction(client, question, str(sample["answers"][id]), pre['summary'])
+            except Exception as e:
+                print(e)
+                ans = "False"
+            # print(result)
+            if not model.endswith('autoagent'):
+                process = [sample["id"], ans, str(sample["answers"][id]), pre['response'][:]]
+            else:
+                process = [sample["id"], ans, str(sample["answers"][id]), pre['summary'][:]]
+
+            result.append(ans)
+            json.dump(process, save_process)
+            save_process.write("\n")
+            save_process.flush()
+    json.dump(result, save_f)
+    save_f.write("\n")
+    save_f.flush()
+    results += result
+
+save_f.close()
+save_process.close()
+
+results_c = []
+for i, result in enumerate(results):
+    if "true" in result.lower():
+        results_c.append(True)
+    else:
+        results_c.append(False)
+
+idx = 0
+score4cha = []
+for sample in tqdm(samples):
+    if len(sample["questions"]) > 0:
+        score_ = sum(results_c[idx:idx+len(sample["questions"])]) / len(sample["questions"])
+        idx += len(sample["questions"])
+        score4cha.append(score_)
+print(f"Accuracy for each challenge is {score4cha}")
+
+
+acc = sum(results_c) / len(results_c)
+print(f"Accuracy for all the {len(results_c)} questions is {acc}")
+

data_analysis/data.json

@@ -0,0 +1,38 @@
+{"id": "00000001", "name": "2016-round-1-section-2-chip-off-the-old-block", "url": "https://www.eloquens.com/tool/vYDF7b/finance/modeloff-sample-past-questions/2016-round-1-section-2-chip-off-the-old-block", "txt": "", "questions": ["question6", "question7", "question8", "question9", "question10", "question11", "question12", "question13", "question14", "question15", "question16", "question17", "question18"], "answers": ["D", "D", "I", "A", "H", "C", "E", "H", "D", "A", "I", 1661626, 323272], "year": 2016}
+{"id": "00000002", "name": "2012-round-1-theory-and-practice-mcqs", "url": "https://www.eloquens.com/tool/01gzcLr1/finance/modeloff-sample-past-questions/2012-round-1-theory-and-practice-mcqs", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10", "question11", "question12", "question13", "question14", "question15", "question16", "question17", "question18", "question19", "question20", "question21", "question22", "question23", "question24", "question25"], "answers": ["C", "D", "A", "C", "D", "C", "D", "C", "D", "C", "C", "B", "A", "B", "D", "C", "D", "A", "C", "C", "A", "D", "C", "D", "A"], "year": 2012}
+{"id": "00000003", "name": "2012-round-2-monte-carlo", "url": "https://www.eloquens.com/tool/YmZXsBaj/finance/modeloff-sample-past-questions/2012-round-2-monte-carlo", "txt": "", "questions": ["question1", "question2", "question3"], "answers": ["D", "B", "C"], "year": 2012}
+{"id": "00000004", "name": "2017-round-1-go-with-the-flow", "url": "https://www.eloquens.com/tool/PG9Hrx/finance/modeloff-sample-past-questions/2017-round-1-go-with-the-flow", "txt": "", "questions": ["question41", "question42", "question43", "question44", "question45", "question46", "question47", "question48", "question49", "question50"], "answers": ["C", "C", "A", "I", "C", "D", "C", "B", "4", "$5753961"], "year": 2017}
+{"id": "00000005", "name": "2016-round-1-section-3-roll-the-dice", "url": "https://www.eloquens.com/tool/J1GcKr/finance/modeloff-sample-past-questions/2016-round-1-section-3-roll-the-dice", "txt": "", "questions": ["question20", "question21", "question22", "question23", "question24", "question25", "question26", "question27", "question28", "question29"], "answers": ["B", "F", "I", "B", "D", "E", "G", "F", 178052, 23], "year": 2016}
+{"id": "00000006", "name": "2016-round-2-section-3-tally-up", "url": "https://www.eloquens.com/tool/3eMfLA/finance/modeloff-sample-past-questions/2016-round-2-section-3-tally-up", "txt": "", "questions": ["question17", "question18", "question19", "question20", "question21", "question22", "question23", "question24", "question25", "question26"], "answers": ["C", "B", "I", "A", "D", "F", "F", "D", 4, "EZTI"], "year": 2016}
+{"id": "00000007", "name": "2017-finals-funding-fun", "url": "https://www.eloquens.com/tool/MEdTY8/finance/modeloff-sample-past-questions/2017-finals-funding-fun", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10", "question11", "question12", "question13", "question14", "question15", "question16", "question17", "question18", "question19", "question20", "question21", "question22", "question23"], "answers": ["E", "C", "D", "31 Dec 2029", "9.424%", "18217 k", "1.539", "9.388%", "30 Sep 2024", "16478 k", "1.588", "22586 k", "31 Mar 2028", "9.088%", "1.493", "10.309%", "1.613", "42695 k", "52481 k", "16074 k", "OS 4, 17236 k", "OS 6, 1.625", "OS 3, 67951 k"], "year": 2017}
+{"id": "00000008", "name": "2017-round-2-section-3-system-allocation", "url": "https://www.eloquens.com/tool/dk9IaJ/finance/modeloff-sample-past-questions/2017-round-2-section-3-system-allocation", "txt": "", "questions": ["question26", "question27", "question28", "question29", "question30", "question31", "question32", "question33", "question34"], "answers": ["C", "I", "B", "A", "I", "E", "$61,868", "F", "$513,000"], "year": 2017}
+{"id": "00000009", "name": "2017-finals-word-play", "url": "https://www.eloquens.com/tool/rXqsJK/finance/modeloff-sample-past-questions/2017-finals-word-play", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10", "question11", "question12", "question13", "question14", "question15"], "answers": [{"Q": 1, "# of words": 7, "Xth word": "ALIBI", "Highest Word": "BIALY", "Highest Score": 21}, {"Q": 2, "# of words": 8, "Xth word": "RARER", "Highest Word": "BARER", "Highest Score": 14}, {"Q": 3, "# of words": 20, "Xth word": "ASSERT", "Highest Word": "MARSES", "Highest Score": 15}, {"Q": 4, "# of words": 7, "Xth word": "LANNER", "Highest Word": "ALTERN", "Highest Score": 9}, {"Q": 5, "# of words": 3, "Xth word": "RIBAND", "Highest Word": "MINBAR", "Highest Score": 17}, {"Q": 6, "# of words": 14, "Xth word": "INARM", "Highest Word": "ABRIM", "Highest Score": 16}, {"Q": 7, "# of words": 7, "Xth word": "RECANTS", "Highest Word": "CANTERS", "Highest Score": 16}, {"Q": 8, "# of words": 9, "Xth word": "CANERS", "Highest Word": "ARCSEC", "Highest Score": 17}, {"Q": 9, "# of words": 14, "Xth word": "TANGI", "Highest Word": "AGING", "Highest Score": 12}, {"Q": 10, "# of words": 6, "Xth word": "DECLAIM", "Highest Word": "CLAIMED", "Highest Score": 21}, {"Q": 11, "# of words": 108, "Xth word": "BASIC", "Highest Word": "SQUAB", "Highest Score": 36}, {"Q": 12, "# of words": 249, "Xth word": "SCARED", "Highest Word": "ZERDAS", "Highest Score": 37}, {"Q": 13, "# of words": 82, "Xth word": "STEEDS", "Highest Word": "KESHES", "Highest Score": 24}, {"Q": 14, "# of words": 115, "Xth word": "FLAME", "Highest Word": "XYLEM", "Highest Score": 36}, {"Q": 15, "# of words": 47, "Xth word": "HINTS", "Highest Word": "INFIX", "Highest Score": 32}], "year": 2017}
+{"id": "00000010", "name": "2017-finals-ladder-up", "url": "https://www.eloquens.com/tool/nLNtkg/finance/modeloff-sample-past-questions/2017-finals-ladder-up", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10", "question11", "question12", "question13", "question14", "question15", "question16", "question17", "question18", "question19", "question20"], "answers": ["C", "Antioch", "D", 78, "Ani", "F", "G", "B", 30, "Naissus", "H", "E", 114284, 98862, "Chalcedon", 100376, 98996, 113542, 14, 41381], "year": 2017}
+{"id": "00000011", "name": "2014-round-2-purple-city", "url": "https://www.eloquens.com/tool/YmZecBal/finance/modeloff-sample-past-questions/2014-round-2-purple-city", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8"], "answers": ["B", "A", "B", "A", "C", "D", "C", "C"], "year": 2014}
+{"id": "00000012", "name": "2015-round-2-more-money-please", "url": "https://www.eloquens.com/tool/R5EH2M/finance/modeloff-sample-past-questions/2015-round-2-more-money-please", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10"], "answers": ["D", "F", "F", "A", "E", "A", "A", "F", "B", "B"], "year": 2015}
+{"id": "00000013", "name": "2017-round-2-late-again", "url": "https://www.eloquens.com/tool/8QEc23/finance/modeloff-sample-past-questions/2017-round-2-late-again", "txt": "", "questions": ["question14", "question15", "question16", "question17", "question18", "question19", "question20", "question21", "question22", "question23", "question24"], "answers": ["I", "E", "F", "H", "B", 34, "G", "D", "C", 298, 42], "year": 2017}
+{"id": "00000014", "name": "2012-round-2-asset-schedule", "url": "https://www.eloquens.com/tool/1zamHG2V/finance/modeloff-sample-past-questions/2012-round-2-asset-schedule", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5"], "answers": ["b", "a", "d", "a", "b"], "year": 2012}
+{"id": "00000016", "name": "2017-round-2-section-1-collect-the-cash", "url": "https://www.eloquens.com/tool/mR9fry/finance/modeloff-sample-past-questions/2017-round-2-section-1-collect-the-cash", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10", "question11", "question12"], "answers": ["D", "I", "F", "E", "H", "E", "D", "H", "A", "$983.01m", "C", "$575.83m"], "year": 2017}
+{"id": "00000017", "name": "2017-round-1-when-it-rains-it-pours", "url": "https://www.eloquens.com/tool/MElhYJ/finance/modeloff-sample-past-questions/2017-round-1-when-it-rains-it-pours", "txt": "", "questions": ["question29", "question30", "question31", "question32", "question33", "question34", "question35", "question36", "question37", "question38", "question39"], "answers": ["E", "B", "I", "C", "E", "A", "F", "F", "G", "A", "D"], "year": 2017}
+{"id": "00000018", "name": "2017-finals-castles-in-the-air", "url": "https://www.eloquens.com/tool/PGqTr5/finance/modeloff-sample-past-questions/2017-finals-castles-in-the-air", "txt": "", "questions": ["question16", "question17", "question18", "question19", "question20", "question21", "question22", "question23", "question24", "question25", "question26", "question27"], "answers": ["H", "E", "D", "A", "B", "H", "2,957", "D", "2,3,4a", "640,121", "D", "D"], "year": 2017}
+{"id": "00000019", "name": "2016-round-2-section-2-fund-the-future", "url": "https://www.eloquens.com/tool/yxXfz3/finance/modeloff-sample-past-questions/2016-round-2-section-2-fund-the-future", "txt": "", "questions": ["question6", "question7", "question8", "question9", "question10", "question11", "question12", "question13", "question14", "question15"], "answers": ["E", "D", "F", "I", "F", "C", "G", "H", 1345868, 2054], "year": 2016}
+{"id": "00000020", "name": "2016-round-2-section-4-maximize-the-benefit", "url": "https://www.eloquens.com/tool/e90sqR/finance/modeloff-sample-past-questions/2016-round-2-section-4-maximize-the-benefit", "txt": "", "questions": ["question28", "question29", "question30", "question31", "question32", "question33", "question34", "question35", "question36", "question37"], "answers": ["E", "A", "H", "E", "C", "F", "D", 14978914492, 12765813502, "E"], "year": 2016}
+{"id": "00000022", "name": "2016-round-1-section-4-going-around-in-circles", "url": "https://www.eloquens.com/tool/jnZsp8/finance/modeloff-sample-past-questions/2016-round-1-section-4-going-around-in-circles", "txt": "", "questions": ["question31", "question32", "question33", "question34", "question35", "question36", "question37", "question38", "question39", "question40"], "answers": ["B", "A", "I", "H", "E", "B", "B", 1175387, 241449527, 85300000], "year": 2016}
+{"id": "00000025", "name": "2015-round-1-tax", "url": "https://www.eloquens.com/tool/L0KSw2/finance/modeloff-sample-past-questions/2015-round-1-tax", "txt": "", "questions": ["question32", "question33", "question34", "question35", "question36", "question37", "question38", "question39", "question40", "question41"], "answers": ["D", "D", "A", "B", "A", "B", "C", "C", "C", "D"], "year": 2015}
+{"id": "00000026", "name": "2015-round-2-the-price-is-right", "url": "https://www.eloquens.com/tool/XRjvi2Rx/finance/modeloff-sample-past-questions/2015-round-2-the-price-is-right", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10", "question11"], "answers": ["B", "D", "E", "B", "A", "C", "B", "D", "A", "F", "A"], "year": 2015}
+{"id": "00000027", "name": "2015-round-1-bread-and-butter", "url": "https://www.eloquens.com/tool/24MvtXKA/finance/modeloff-sample-past-questions/2015-round-1-bread-and-butter", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10", "question11", "question12", "question13", "question14", "question15"], "answers": ["B", "D", "F", "F", "D", "B", "F", "A", "C", "D", "C", "B", "B", "E", "E"], "year": 2015}
+{"id": "00000028", "name": "2015-round-one-options-to-call", "url": "https://www.eloquens.com/tool/BNQmf7ng/finance/modeloff-sample-past-questions/2015-round-one-options-to-call", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10"], "answers": ["B", "C", "D", "C", "B", "F", "A", "D", "F", "F"], "year": 2015}
+{"id": "00000029", "name": "2014-round-2-stepping-up", "url": "https://www.eloquens.com/tool/1zaMCG2y/finance/modeloff-sample-past-questions/2014-round-2-stepping-up", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10"], "answers": ["A", "B", "C", "E", "C", "A", "F", "B", "F", "F"], "year": 2014}
+{"id": "00000030", "name": "2014-round-2-time-is-money", "url": "https://www.eloquens.com/tool/01g0uLrD/finance/modeloff-sample-past-questions/2014-round-2-time-is-money", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7"], "answers": ["D", "A", "B", "A", "C", "B", "C"], "year": 2014}
+{"id": "00000031", "name": "2014-round-1-snakes-and-ladders", "url": "https://www.eloquens.com/tool/E1zkcxBy/finance/modeloff-sample-past-questions/2014-round-1-snakes-and-ladders", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5"], "answers": ["C", "B", "B", "B", "A"], "year": 2014}
+{"id": "00000032", "name": "2014-round-1-dealing-with-data", "url": "https://www.eloquens.com/tool/xKRGSPm3/finance/modeloff-sample-past-questions/2014-round-1-dealing-with-data", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7"], "answers": ["B", "D", "A", "A", "B", "A", "C"], "year": 2014}
+{"id": "00000033", "name": "2014-round-1-precise-debt-modeling", "url": "https://www.eloquens.com/tool/gyD1iM3L/finance/modeloff-sample-past-questions/2014-round-1-precise-debt-modeling", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8"], "answers": ["C", "D", "A", "C", "B", "B", "B", "A"], "year": 2014}
+{"id": "00000034", "name": "2013-round-2-data-analysis", "url": "https://www.eloquens.com/tool/GYVEIgMl/finance/modeloff-sample-past-questions/2013-round-2-data-analysis", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10", "question11", "question12", "question13", "question14", "question15", "question16"], "answers": ["B", "A", "A", "C", "D", "C", "C", "A", "C", "A", "C", "A", "C", "A", "B", "B"], "year": 2013}
+{"id": "00000035", "name": "2013-round-2-hard-times-turnaround-a-toy-company", "url": "https://www.eloquens.com/tool/7lpLu3B8/finance/modeloff-sample-past-questions/2013-round-2-hard-times-turnaround-a-toy-company", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9"], "answers": ["A", "C", "A", "B", "B", "A", "C", "A", "B"], "year": 2013}
+{"id": "00000036", "name": "2013-round-2-theory-and-practice", "url": "https://www.eloquens.com/tool/5M3AiQaL/finance/modeloff-sample-past-questions/2013-round-2-theory-and-practice", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10", "question11", "question12", "question13", "question14", "question15"], "answers": ["C", "B", "B", "A", "D", "A", "B", "A", "C", "C", "D", "B", "A", "D", "C"], "year": 2013}
+{"id": "00000037", "name": "2013-round-1-theory-and-practice", "url": "https://www.eloquens.com/tool/JkP9f1RX/finance/modeloff-sample-past-questions/2013-round-1-theory-and-practice", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10", "question11", "question12", "question13", "question14", "question15", "question16", "question17", "question18", "question19", "question20"], "answers": ["D", "C", "D", "C", "D", "D", "C", "A", "D", "C", "A", "C", "D", "B", "A", "B", "C", "A", "C", "C"], "year": 2013}
+{"id": "00000038", "name": "2013-round-1-energy-operations", "url": "https://www.eloquens.com/tool/3BwKHyKd/finance/modeloff-sample-past-questions/2013-round-1-energy-operations", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5"], "answers": ["D", "A", "A", "B", "A"], "year": 2013}
+{"id": "00000039", "name": "2013-round-1-acquisition-financing", "url": "https://www.eloquens.com/tool/qklauxkL/finance/modeloff-sample-past-questions/2013-round-1-acquisition-financing", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6"], "answers": ["A", "C", "C", "D", "C", "C"], "year": 2013}
+{"id": "00000041", "name": "2012-finals-investment-property", "url": "https://www.eloquens.com/tool/XRjqu2RY/finance/modeloff-sample-past-questions/2012-finals-investment-property", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5"], "answers": ["A", "A", "D", "B", "A"], "year": 2012}
+{"id": "00000042", "name": "2012-finals-excel-knowledge-test", "url": "https://www.eloquens.com/tool/24M1hXK3/finance/modeloff-sample-past-questions/2012-finals-excel-knowledge-test", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10", "question11", "question12", "question13", "question14", "question15", "question16", "question17", "question18", "question19", "question20", "question21", "question22", "question23", "question24", "question25", "question26", "question27", "question28", "question29", "question30", "question31", "question32", "question33", "question34", "question35", "question36", "question37", "question38", "question39", "question40", "question41", "question42", "question43", "question44", "question45", "question46", "question47", "question48", "question49", "question50"], "answers": ["CONCATENATE", "LEFT", "LEN", "MID", "PROPER", "REPLACE", "RIGHT", "TRIM", "EDATE", "EOMONTH", "NETWORKDAYS", "WEEKNUM", "WORKDAY", "YEARFRAC", "GETPIVOTDATA", "HLOOKUP", "INDEX", "INDIRECT", "MATCH", "OFFSET", "TRANSPOSE", "ISTEXT", "ABS", "CEILING", "COMBIN", "EVEN", "EXP", "FACT", "FLOOR", "INT", "LN", "LOG", "MOD", "MROUND", "ODD", "PI", "POWER", "RANDBETWEEN", "ACCRINT", "DB", "DOLLARDE", "EFFECT", "FV", "IPMT", "IRR", "MIRR", "PRICE", "RATE", "XIRR", "NPV"], "year": 2012}
+{"id": "00000043", "name": "2012-round-2-find-that-error", "url": "https://www.eloquens.com/tool/BNQEf7nw/finance/modeloff-sample-past-questions/2012-round-2-find-that-error", "txt": "", "questions": ["question1", "question2", "question3", "question4", "question5", "question6", "question7", "question8", "question9", "question10", "question11", "question12", "question13", "question14", "question15", "question16", "question17", "question18", "question19", "question20"], "answers": ["B", "B", "C", "C", "C", "A", "B", "A", "B", "B", "A", "A", "C", "D", "D", "A", "B", "B", "D", "A"], "year": 2012}

data_analysis/data/00000001/introduction.txt

@@ -0,0 +1,74 @@
+INTRODUCTION
+All the inputs mentioned below are provided in the workbook for this case study. 
+You are working for a company that is planning to bid for a 20% stake in the cashflows of a biomass plant 
+which burns wood chip to produce electricity. You have been asked to predict the cashflows of the project 
+from 1 January 2017 until 31 December 2026, using a quarterly model, and to use your model to 
+recommend to your CEO the purchase price she should offer for the 20% stake. 
+You should assume that all invoices are settled in the same quarter they are issued, there are no 
+inventory requirements and no taxes are applicable. 
+Where amounts are to be inflated they are given in 2017 prices and inflation should be applied on 1 
+January of each subsequent year.  Do NOT round inflated prices to whole cents in interim calculations. 
+ELECTRICITY GENERATION 
+The plant is a 9MW plant; i.e. if it is running at full capacity it will produce 9MWh of electricity per hour. 
+Your company’s engineers think it is reasonable to assume that the plant will usually run at 95% of its 
+capacity, 24 hours a day. 
+There are several planned shutdown periods when no electricity will be produced at all from the start of 
+the first day (at midnight) until the end of the final day (at midnight). 
+
+First day of shutdown     Final day of shutdown
+1 June 2017               15 June 2017
+15 December 2019          3 January 2020
+5 April 2021              25 April 2021
+25 October 2023           10 November 2023
+6 May 2025                26 May 2025
+26 June 2026              5 July 2026
+
+
+SALE OF ELECTRICITY 
+Electricity is sold as follows: 
+• At the market price if this is between the cap price and the floor price.
+The predictions for the market price have been provided in the workbook accompanying this 
+question on an April -March annual timeline, and should not be inflated. 
+• At the cap price if the market price is higher than the cap price.
+The cap price is $70 per MWh, inflated at 2% per annum. 
+• At the floor price if the market price is lower than the floor price.
+The floor price is $45 per MWh, inflated at 1% per annum.
+ 
+Your CEO is concerned that the market price predictions for the sale of electricity may be overly optimistic.  
+She has therefore asked you to run some of the analysis (see Questions 5, 11 and 13) as though 
+electricity were always sold at the floor price.  It will be beneficial if you develop your model in a way that 
+makes it easy to switch between modelling the electricity price as described above and modelling only the 
+floor price. 
+PURCHASE OF WOOD CHIP 
+In each quarter, the project purchases exactly enough wood chip to produce the MWh output of the plant 
+in that quarter. 
+The plant has two possible wood chip suppliers. The project first buys the wood chip from the supplier that 
+gives them the lowest cost per MWh in that quarter, and, if necessary, tops this up with wood chip from 
+the other supplier. 
+The first supplier sells wood chip on the following terms: 
+• Wood chip costs $100 per tonne, inflated at 2% per annum
+• 1 tonne of wood chip will produce 3.5 MWh of electricity
+• Up to 4,500 tonnes may be purchased per quarter
+The second supplier sells wood chip on the following terms: 
+• Wood chip costs $130 per tonne. This is a fixed price that should not be inflated
+• 1 tonne of wood chip will produce 4 MWh of electricity
+• An unlimited amount of wood chip may be purchased per quarter
+FIXED COSTS 
+The project incurs $75,000 of fixed costs per month. This amount is indexed at 1.5%. 
+VALUATION 
+The company’s required rate of return for this project is 10%, and you should assume that there is no 
+terminal value or disposal costs for the 20% stake at the end of the ten years. 
+To determine the purchase price your company should offer: 
+i) Discount using an Excel function (or functions) that discounts on an actual/365 basis;
+ii) Assume an acquisition date of 31 December 2016; and
+iii) Assume that all cashflows occur at the end of the quarter in which they occur.
+ 
+GENERAL COMMENTS 
+When used in the question wording, “Q1” refers to the period January to March, “Q2” to the period April to 
+June, “Q3” to the period July to September and “Q4” to the period October to December. 
+ 
+For Questions 6 to 17, perform your analysis assuming a 10% required rate of return. 
+For Question 18, perform your analysis assuming a 4% required rate of return. 
+ 
+For Questions 6 to 16, select your answer from a multiple choice list. 
+For Questions 17 to 18, you are required to type in your answer.

data_analysis/data/00000001/question10.txt

@@ -0,0 +1,13 @@
+
+Question 10
+What is the total revenue earned through the sale of electricity over the ten years based on the
+floor price only?
+A. $34,274,773
+B. $34,274,774
+C. $34,274,775
+D. $34,274,776
+E. $34,274,777
+F. $34,274,778
+G. $34,274,779
+H. $34,274,780
+I. $34,274,781

data_analysis/data/00000001/question11.txt

@@ -0,0 +1,13 @@
+
+Question 11
+On which quarter start date does the wood chip from supplier 2 first become cheaper per MWh
+produced than the wood chip from supplier 1?
+A. 1 Jul 2023
+B. 1 Oct 2023
+C. 1 Jan 2024
+D. 1 Apr 2024
+E. 1 Jul 2024
+F. 1 Oct 2024
+G. 1 Jan 2025
+H. 1 Apr 2025
+I. 1 Jul 2025

data_analysis/data/00000001/question12.txt

@@ -0,0 +1,12 @@
+
+Question 12
+What is the total cost of wood chip purchased in Q1 2018?
+A. $547,331
+B. $547,332
+C. $547,333
+D. $547,334
+E. $547,335
+F. $547,336
+G. $547,337
+H. $547,338
+I. $547,339

data_analysis/data/00000001/question13.txt

@@ -0,0 +1,12 @@
+
+Question 13
+What is the total cost of wood chip purchased in Q3 2023?
+A. $608,439
+B. $608,440
+C. $608,441
+D. $608,442
+E. $608,443
+F. $608,444
+G. $608,445
+H. $608,446
+I. $608,447

data_analysis/data/00000001/question14.txt

@@ -0,0 +1,12 @@
+
+Question 14
+How many tonnes of wood chip are purchased from supplier 2 over the ten years?
+A. 72,325
+B. 72,326
+C. 72,327
+D. 72,328
+E. 72,329
+F. 72,330
+G. 72,331
+H. 72,332
+I. 72,333

data_analysis/data/00000001/question15.txt

@@ -0,0 +1,14 @@
+
+Question 15
+What are the total revenues less total costs of the project over the ten years based on the
+revenues calculated using the market price subject to a floor and a cap? Round your answer to the
+nearest whole dollar.
+A. $12,829,511
+B. $12,829,512
+C. $12,829,513
+D. $12,829,514
+E. $12,829,515
+F. $12,829,516
+G. $12,829,517
+H. $12,829,518
+I. $12,829,519

data_analysis/data/00000001/question16.txt

@@ -0,0 +1,13 @@
+
+Question 16
+What are the total revenues less total costs of the project over the ten years based on the
+revenues calculated using the floor price only? Round your answer to the nearest whole dollar.
+A. $1,927,596
+B. $1,927,597
+C. $1,927,598
+D. $1,927,599
+E. $1,927,600
+F. $1,927,601
+G. $1,927,602
+H. $1,927,603
+I. $1,927,604

data_analysis/data/00000001/question17.txt

@@ -0,0 +1,7 @@
+
+Question 17
+What price should your company offer for a 20% stake in the plant in order to earn the 10%
+required rate of return based on the revenues calculated using the market price subject to a floor
+and a cap? Round your answer to the nearest whole dollar.
+Type in your answer, without any dollar signs or thousands separator.  E.g. $1,000,000 should be typed
+as 1000000.

data_analysis/data/00000001/question18.txt

@@ -0,0 +1,7 @@
+
+Question 18
+What price should your company offer for a 20% stake in the plant in order to earn the 4% required
+rate of return based on the revenues calculated using the floor price only? Round your answer to
+the nearest whole dollar.
+Type in your answer, without any dollar signs or thousands separator.  E.g. $1,000,000 should be typed
+as 1000000.

data_analysis/data/00000001/question6.txt

@@ -0,0 +1,12 @@
+
+Question 6
+How many days is the plant operational in Q4 2023?
+A. 72
+B. 73
+C. 74
+D. 75
+E. 76
+F. 77
+G. 78
+H. 79
+I. 80

data_analysis/data/00000001/question7.txt

@@ -0,0 +1,12 @@
+
+Question 7
+How many MWh of electricity are produced in total over the ten years?
+A. 728,047
+B. 728,048
+C. 728,049
+D. 728,050
+E. 728,051
+F. 728,052
+G. 728,053
+H. 728,054
+I. 728,055

data_analysis/data/00000001/question8.txt

@@ -0,0 +1,12 @@
+
+Question 8
+What is the cap price in April 2025?
+A. $81.94
+B. $81.95
+C. $81.96
+D. $81.97
+E. $81.98
+F. $81.99
+G. $82.00
+H. $82.01
+I. $82.02

data_analysis/data/00000001/question9.txt

@@ -0,0 +1,13 @@
+
+Question 9
+What is the total revenue earned through the sale of electricity over the ten years based on the
+market price inputs subject to the floor and cap?
+A. $45,176,687
+B. $45,176,688
+C. $45,176,689
+D. $45,176,690
+E. $45,176,691
+F. $45,176,692
+G. $45,176,693
+H. $45,176,694
+I. $45,176,695

data_analysis/data/00000002/introduction.txt

@@ -0,0 +1,3 @@
+INTRODUCTION
+Answer each of the following questions relating to Financial Modeling Theory and Practice.  
+ 

data_analysis/data/00000002/question1.txt

@@ -0,0 +1 @@
+Question 1 What is a Financial Modeler's main objective when gathering internal information for a valuation? a. To examine historical and projected financial data including Financial Statements, Income Tax Returns and Budgets b. To keep abreast of changing general economics to ascertain how it may impact the assumptions that are made during the valuation process c. Thorough analysis to gain an adequate understanding of the subject company’s operational management and earnings ability d. To examine the current location(s) and physical condition of the subject company’s facilities and operational assets

data_analysis/data/00000002/question10.txt

@@ -0,0 +1 @@
+Question 10 When valuing the stock of a real estate holding company, most likely the Financial Modeler will give the greatest weight to which method? a. Capitalization of earnings method b. Book value method c. Adjusted book value method d. Rule of thumb    

data_analysis/data/00000002/question11.txt

@@ -0,0 +1 @@
+Question 11 What is the best way to determine if a normalizing adjustment should be made to accounts receivable? a. Common size the balance sheet b. Use trend analysis c. Look at accounts receivable ageing d. None of the above

data_analysis/data/00000002/question12.txt

@@ -0,0 +1 @@
+Question 12 Which one of the following is not a general category of normalized adjustments? a. Removal of excess cash b. Inventory adjustment when inventory is recorded on a FIFO basis c. Reasonable compensation for owners d. Bad debt adjustment for a significant write off due to an unexpected bankruptcy filing by a major customer

data_analysis/data/00000002/question13.txt

@@ -0,0 +1 @@
+Question 13 Which of the following are typical categories to review in normalizing accounts for a valuation? a. Doubtful accounts, notes receivable, leases b. Notes receivable, leases, compensation but not liabilities and insurance c. Inventory methods, depreciation methods but not non-operating items d. Capitalization and expensing, recognition of income, and expenses and taxes but not nonrecurring or extraordinary costs     

data_analysis/data/00000002/question14.txt

@@ -0,0 +1 @@
+Question 14 The next two questions use the following information (i.e Question 14 and 15). Tom's Mining Equipment Inc has the following historical earnings: Year Earnings ($m) 1 $75,400 2 $65,200 3 $87,600 4  $90,500 5  $53,900 Which method of projecting earnings would appear most appropriate to estimate future benefits? a. Geometric progression b. Unweighted average method c. Trend Line – static method d. All of the above

data_analysis/data/00000002/question15.txt

@@ -0,0 +1 @@
+Question 15 Estimate the future benefits, using the method you have selected in the previous question. Tom's Mining Equipment Inc has the following historical earnings: Year Earnings ($m) 1 $75,400 2 $65,200 3 $87,600 4 $90,500 5 $53,900 a. $73,340m b. $75,700m c. $93,150m d. $74,520m

data_analysis/data/00000002/question16.txt

@@ -0,0 +1 @@
+Question 16 Which of the following is the concatenating operator? a. Apostrophe (‘) b. Exclamation (!) c. Ampersand (&) d. Hash (#)

data_analysis/data/00000002/question17.txt

@@ -0,0 +1 @@
+Question 17 Which Excel feature may cause large financial models to recalculate slowly? a. Array formulas b. Volatile functions c. Data tables d. All of the above

data_analysis/data/00000002/question18.txt

@@ -0,0 +1 @@
+Question 18 The formula =$A$7+$A$8 uses? a. Absolute cell references b. Relative cell references c. Mixed cell references d. Indirect cell references

data_analysis/data/00000002/question19.txt

@@ -0,0 +1 @@
+Question 19 A combination of the following functions is often used in place of a VLOOKUP and HLOOKUP? a. CHOOSE and INDIRECT b. SUMIF and INDEX c. INDEX and MATCH d. OFFSET and COUNTIF

data_analysis/data/00000002/question2.txt

@@ -0,0 +1 @@
+Question 2 What is not a benefit of Financial Modeling standards? a. Improved quality and transparency b. Decreased development time and costs c. Minimization of error risk d. Enforcement of model redundancy       

data_analysis/data/00000002/question20.txt

@@ -0,0 +1 @@
+Question 20 If the third argument in an INDEX function is left blank? a. The first cell in the column is returned b. The last cell in the column is returned c. The entire column is returned d. An error is returned

data_analysis/data/00000002/question21.txt

@@ -0,0 +1 @@
+Question 21 Which of the following functions can be used in a simple formula that uses no other functions to return the first day of the following month? a. EOMONTH b. NETWORKDAYS c. DATE d. DATEVALUE

data_analysis/data/00000002/question22.txt

@@ -0,0 +1 @@
+Question 22 Which function can be used to simplify the following formula? =IF(A1=1,C1+D1,IF(A1=2,C2+D2,IF(A1=3,C3+C1,IF(A1=4,C1+D2,NA())))) a. VLOOKUP b. OFFSET c. SUMIF d. CHOOSE

data_analysis/data/00000002/question23.txt

@@ -0,0 +1 @@
+Question 23 Which of the following functions is not volatile in recent versions of Excel? a. OFFSET b. INDIRECT c. INDEX d. RAND

data_analysis/data/00000002/question24.txt

@@ -0,0 +1 @@
+Question 24 Which is not a common loop structure used in VBA? a. DO WHILE ... LOOP b. FOR ... NEXT c. DO ... LOOP UNTIL d. SELECT ... CASE

data_analysis/data/00000002/question25.txt

@@ -0,0 +1 @@
+Question 25 By default, Excel VBA passes arguments to functions and procedures by? a. Reference b. Value c. Absolute d. Relative

data_analysis/data/00000002/question3.txt

@@ -0,0 +1 @@
+Question 3 The formula to calculate the percentage growth in sales is? a. Period 2 net sales less period 1 net sales divided by period 1 net sales b. Period 1 net sales less period 2 net sales divided by period 1 net sales c. Period 1 net sales less period 2 net sales divided by period 2 net sales d. Period 2 net sales less period 1 net sales divided by period 2 net sales

data_analysis/data/00000002/question4.txt

@@ -0,0 +1 @@
+Question 4 The most conservative ratio in measuring a company’s solvency is? a. Current ratio b. Quick ratio c. Cash ratio d. Turnover ratio

data_analysis/data/00000002/question5.txt

@@ -0,0 +1 @@
+Question 5 Which method combines the income and asset based approaches to arrive at a value of a closely held business? a. Book value b. Discounted cash flows c. Guideline public companies d. Excess earnings method

data_analysis/data/00000002/question6.txt

@@ -0,0 +1 @@
+Question 6 The cost to replace an asset under a particular fact situation is known as? a. Fair market value b. Fair value c. Replacement cost d. Strategic value    

data_analysis/data/00000002/question7.txt

@@ -0,0 +1 @@
+Question 7 During a Mergers and Acquisitions (M&A) transaction, the ability to find and use good comparable data for a valuation is? a. Relatively easy because each successful company within an industry uses the same ratios b. Relatively easy because public stock price fluctuation is not sufficient or erratic enough to make a difference c. Relatively difficult because book value is adjusted in small companies as FIFO (first-in first-out) is the method of choice and in public companies book value is static due to LIFO (last-in-first our method) d. d. Relatively difficult because size differential, management depth product diversity and access to lines of credit will seldom match the company you are valuing

data_analysis/data/00000002/question8.txt

@@ -0,0 +1 @@
+Question 8 When analyzing comparable companies, which valuation discount may be appropriate for a lack of liquidity in a private company? a. Discount for lack of mobility b. Discount for lack of minority interest c. Discount for lack of marketability d. Discount for lack of maintenance

data_analysis/data/00000002/question9.txt

@@ -0,0 +1 @@
+Question 9 In addition to control and marketability discounts/premiums, other modifications may include? a. Key person discounts b. Lack of diversification discount c. Build-in gains discounts d. A and B