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Portfolio Analytics in Accounting, Finance and Economics
  • 7

  • Course Code: FINM4100
  • University: Kaplan Business School
  • Country: Australia

Your Task

Create a slide deck which represents a portfolio of analytics methods used in accounting, economics or finance. This task is to be done individually. Submit your slides at the end of class .

Assessment Instructions

As an individual:

•    You will choose five analytics methods and a financial, accounting or economics application for each method.

•    Out of the five methods that you chose, you are required to investigate one in more detail.

•    Reflect on the limitations of the methods and possible ethical, legal or privacy issues.

Slide creation 

Your presentation should include the following slide format:

•    Title, student name and ID 

•    Summary slide of the analytics methods selected and applications 

•    Analytics methods 1 to 4

o    Create one slide for the analytics method and one for the application

•    Analytics method 5

o    create two slides for the analytics method and one for the application 

•    Reflect and list the limitations of the analytics methods chosen 

•    Discuss in short sentence form possible ethical, legal and privacy issues 

ANSWERS

Overview of Analytics Methods and Applications

•    Time Series Analysis: Applied for Forecasting Stock Prices in Finance (Sezer et al., 2020).
•    Regression Analysis: Utilized to Examine the Relationship between Interest Rates and Investment Spending in Economics (Mohsin et al., 2021).
•    Monte Carlo Simulation: Employed for Risk Assessment in Investment Portfolios (Shadabfar & Cheng, 2020).
•    Data Envelopment Analysis: Used for Measuring Efficiency of Production in Firms in Economics (Kaffash et al., 2020).
•    Machine Learning (Decision Trees): Implemented for Predicting Bankruptcy in Accounting (Soui et al., 2019).

Time Series Analysis

•    Definition: Time Series Analysis involves studying ordered, time-based data points to identify trends, seasonal patterns, and cyclic behaviors (Sezer et al., 2020).
•    Purpose: It is pivotal in various fields, especially in finance, for forecasting future stock prices and market trends (Barra et al., 2020).
•    Techniques: Autoregressive Integrated Moving Average (ARIMA), Seasonal Decomposition of Time Series (STL), and Exponential Smoothing State Space Model (ETS) are common techniques in Time Series Analysis (Yu et al., 2021).

Application of Time Series Analysis – Forecasting Stock Prices

•    Application: By analyzing historical price data and identifying patterns, Time Series Analysis helps investors predict future stock prices (Ananthi & Vijayakumar, 2020).
•    Benefit: This allows investors and financial analysts to make informed investment decisions and optimize portfolio performance (Shanmuganathan, 2020).
•    Example: Forecasting the stock prices of tech companies like Apple and Amazon to strategize investment (Masih et al., 2021).

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Regression Analysis

•    Definition: Regression Analysis is a statistical method that investigates the relationships among variables (Mohsin et al., 2021). It is used to understand how the dependent variable changes when one of the independent variables is varied.
•    Purpose: Widely used in economics and finance to model and analyze the relationships between a dependent variable and one or more independent variables (Sunardi & Tatariyanto, 2023).
•    Techniques: Linear Regression, Multiple Regression, and Logistic Regression are common forms of regression analysis (Ali & Younas, 2021).

Application of Regression Analysis – Interest Rates and Investment Spending

•    Application: Regression Analysis models the relationship between interest rates and investment spending, which is crucial for economic policy (Ali & Younas, 2021).
•    Benefit: Policymakers and economists can understand how variations in interest rates impact investment spending and economic activity (Akron et al., 2020).
•    Example: Central banks might use regression analysis to predict how a change in the base interest rate will affect investment levels in the economy (Basten & Mariathasan, 2023).

Monte Carlo Simulation

•    Definition: Monte Carlo Simulation is a computational technique that uses randomness to solve problems that might be deterministic in principle (Shadabfar & Cheng, 2020).
•    Purpose: It allows for the risk assessment of various decisions by running multiple simulations and calculating the probability distribution of possible outcomes (Naderpour et al., 2019).
•    Techniques: Monte Carlo Simulation relies on random input generation, probability distributions, and statistical analysis (Zhang, 2020).
Application of Monte Carlo Simulation – Risk Assessment in Investment Portfolios
•    Application: Investors use Monte Carlo Simulation to evaluate risk by modelling hypothetical market conditions and projecting the probability of various outcomes (Fabianová et al., 2023).
•    Benefit: As a result, investors can weigh the potential benefits against the potential drawbacks of various investment strategies (Shi et al., 2022).
•    Example: Production efficiency can be measured in terms of multiple inputs and outputs using DEA, which is a non-parametric linear programming method (Kaffash et al., 2020).

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Data Envelopment Analysis (DEA)

•    Definition: DEA is a non-parametric linear programming method for measuring the efficiency of production in terms of multiple inputs and outputs (Kaffash et al., 2020).
•    Purpose: It is used in economics to evaluate the relative efficiency of firms, identifying best practices and areas for improvement (De Luca et al., 2022).
•    Techniques: Input-oriented and output-oriented DEA models are commonly used to measure efficiency (Li & Tsai, 2018).

Application of Data Envelopment Analysis – Measuring Firm Efficiency

•    Application: DEA is employed to assess the efficiency of firms by comparing the input and output ratios of different entities (Smriti & Khan, 2021).
•    Benefit: Identifying efficient firms and best practices enables benchmarking and improvement of performance across the industry (Rahim & Shah, 2019).
•    Example: A regulatory agency might use DEA to assess the efficiency of utility companies to ensure optimal resource allocation (De Luca et al., 2022).

Machine Learning – Decision Trees

•    Definition: Decision Trees are a type of supervised machine learning algorithm used for classification and regression tasks (Soui et al., 2019).
•    Purpose: They are valuable in accounting and finance for predicting outcomes and aiding in decision-making processes (Wang et al., 2020).
•    Techniques: CART (Classification and Regression Trees) and ID3 (Iterative Dichotomiser 3) are common algorithms for building decision trees (Kori & Kakkasageri, 2023).

Application of Decision Trees – Predicting Bankruptcy

•    Application: Decision Trees analyze features of companies to predict the likelihood of bankruptcy, assisting in risk assessment (Wang et al., 2022).
•    Benefit: Financial institutions and investors can identify high-risk entities and make informed lending and investment decisions (Setyowati, 2020).
•    Example: A bank might use decision trees to assess the creditworthiness of a business applying for a loan (Madaan et al., 2021).
Monte Carlo Simulation – Detailed Investigation
•    Definition: Monte Carlo Simulation involves generating a large number of random inputs to model the behavior of a financial system (An et al., 2021).
•    Process: Statistical experiments informed by chance play a central role in this approach to problem-solving. Multiple iterations are performed, each with a different set of inputs chosen at random (Kadkhodaei et al., 2022).
•    Outcome Analysis: By examining the range of possible results, investors can better understand the likelihood of various scenarios and evaluate risk and return (Dao et al., 2020).
•    Use in Finance: Option pricing, portfolio management, and risk management are all areas where Monte Carlo Simulation has proven invaluable owing to its widespread use in finance. (Zhuang & Tang, 2023).

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Monte Carlo Simulation – Techniques and Applications

•    Techniques: Financial market volatility can be simulated with the help of probability distributions, random number generation, and statistical models (Shadabfar & Cheng, 2020).
•    Risk Assessment: It is a crucial tool for evaluating the risk and uncertainty of financial instruments and investment portfolios (Senova et al., 2023).
•    Option Pricing: As stated by BOYARCHENKO and LEVENDORSKI (2019), "Option pricing is widely used for pricing complex financial derivatives and understanding the implications of different market conditions."
•    Scenario Analysis: Allows for scenario analysis by modeling the results of potential future events on financial assets, such as market fluctuations and interest rate shifts (Lokesh, 2022).

Application of Monte Carlo Simulation – Risk Assessment in Investment Portfolios

•    Scenario Analysis: Investors use Monte Carlo Simulation to perform extensive scenario analysis, evaluating how portfolios might perform under diverse market conditions (Aho, 2023).
•    Portfolio Optimization: By understanding the distribution of portfolio returns, investors can optimize asset allocation to achieve desired risk-return profiles (Muganda & Kasamani, 2023).
•    Decision Making: The insights derived from the simulation aid investors in making informed investment decisions, managing risk, and enhancing portfolio performance (Alaminos et al., 2023).
•    Real-world Example: Hedge funds and asset managers routinely employ Monte Carlo Simulation for strategic asset allocation and risk management (Sobieraj & Metelski, 2022).

Reflection on Limitations – Limitations of Selected Analytics Methods

•    Time Series Analysis: Relies on historical data and assumes past patterns will continue, which might not always be accurate (Vaidya, 2023). Sensitive to outliers and can be affected by non-stationarity.
•    Regression Analysis: Sensitive to outliers and can suffer from omitted variable bias, multicollinearity, and overfitting (Yu et al., 2022).
•    Monte Carlo Simulation: Highly dependent on the quality of input data and assumptions made. Requires extensive computational resources (Chauvin et al., 2020).
•    Data Envelopment Analysis: Efficiency scores are relative and can be sensitive to the choice of inputs and outputs (Salas-Velasco, 2019). Does not account for statistical noise (Banker et al., 2019).
•    Decision Trees: Prone to overfitting, especially on noisy datasets (Yuvaraj et al., 2021). Biased towards dominant classes and might not perform well with unstructured data (Chabbouh et al., 2019).

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Ethical, Legal, and Privacy Considerations

•    Data Privacy: Analysts must ensure the confidentiality and privacy of the data used in analysis to protect individuals’ information and comply with data protection regulations (Menges et al., 2021).
•    Bias and Fairness: It is crucial to identify and mitigate biases in analytical models to prevent unfair or discriminatory outcomes (van Giffen et al., 2022).
•    Transparency and Explainability: Providing clear explanations of the analytics methods and models used is essential for maintaining trust and ensuring responsible decision-making (Bharadiya, 2023).

Navigating Ethical and Legal Challenges

•    Regulatory Compliance: Analysts must adhere to relevant laws and regulations related to data usage, analytics, and financial reporting (Bondoc & Taicu, 2019).
•    Informed Consent: When using personal data, obtaining informed consent and ensuring that individuals are aware of how their data will be used is vital (Behrendt & Loh, 2022).
•    Accountability and Responsibility: Analysts and organizations must take responsibility for the outcomes of the analytics and address any negative consequences or inaccuracies (Beatty et al., 2021). 

References

Aho, A. (2023). A Statistical Analysis of Weighting Techniques for Portfolio Construction: Insights for Portfolio Managers and Investors. https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1768567&dswid=4401
Akron, S., Demir, E., Díez-Esteban, J. M., & García-Gómez, C. D. (2020). Economic policy uncertainty and corporate investment: Evidence from the U.S. Hospitality Industry. Tourism Management, 77, 104019. https://doi.org/10.1016/j.tourman.2019.104019
Alaminos, D., Salas, M. B., & Fernández-Gámez, M. (2023). Quantum Monte Carlo simulations for estimating Forex Markets: A speculative attacks experience. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-01836-2
Ali, P., & Younas, A. (2021). Understanding and interpreting regression analysis. Evidence Based Nursing, 24(4), 116–118. https://doi.org/10.1136/ebnurs-2021-103425
An, D., Linden, N., Liu, J.-P., Montanaro, A., Shao, C., & Wang, J. (2021). Quantum-accelerated multilevel Monte Carlo methods for stochastic differential equations in mathematical finance. Quantum, 5, 481. https://doi.org/10.22331/q-2021-06-24-481
Ananthi, M., & Vijayakumar, K. (2020). Retracted article: Stock market analysis using candlestick regression and market trend prediction (CKRM). Journal of Ambient Intelligence and Humanized Computing, 12(5), 4819–4826. https://doi.org/10.1007/s12652-020-01892-5
Banker, R., Natarajan, R., & Zhang, D. (2019). Two-stage estimation of the impact of contextual variables in stochastic frontier production function models using data envelopment analysis: Second Stage OLS versus bootstrap approaches. European Journal of Operational Research, 278(2), 368–384. https://doi.org/10.1016/j.ejor.2018.10.050
Barra, S., Carta, S. M., Corriga, A., Podda, A. S., & Recupero, D. R. (2020). Deep learning and time series-to-image encoding for financial forecasting. IEEE/CAA Journal of Automatica Sinica, 7(3), 683–692. https://doi.org/10.1109/jas.2020.1003132
Basten, C., & Mariathasan, M. (2023). Interest rate pass-through and bank risk-taking under negative-rate policies with tiered remuneration of Central Bank Reserves. Journal of Financial Stability, 68, 101160. https://doi.org/10.1016/j.jfs.2023.101160
Beatty, A. L., Peyser, N. D., Butcher, X. E., Cocohoba, J. M., Lin, F., Olgin, J. E., Pletcher, M. J., & Marcus, G. M. (2021). Analysis of COVID-19 vaccine type and adverse effects following vaccination. JAMA Network Open, 4(12). https://doi.org/10.1001/jamanetworkopen.2021.40364
Behrendt, H., & Loh, W. (2022). Informed consent and algorithmic discrimination – is giving away your data the new vulnerable? Review of Social Economy, 80(1), 58–84. https://doi.org/10.1080/00346764.2022.2027506
Bharadiya, J. P. (2023). Machine Learning and AI in Business Intelligence: Trends and Opportunities. International Journal of Computer (IJC), 48(1), 123-134. https://www.researchgate.net/profile/Jasmin-Bharadiya-4/publication/371902170_Machine_Learning_and_AI_in_Business_Intelligence_Trends_and_Opportunities/
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Bondoc, M. D., & Taicu, M. (2019). Ethics in financial reporting and organizational communication. Scientific Bulletin-Economic Sciences, 18(3), 168-174. http://economic.upit.ro/RePEc/pdf/2019_3_22.pdf
BOYARCHENKO, S., & LEVENDORSKIĬ, S. (2019). Sinh-acceleration: Efficient evaluation of probability distributions, option pricing, and Monte Carlo simulations. International Journal of Theoretical and Applied Finance, 22(03), 1950011. https://doi.org/10.1142/s0219024919500110
Chabbouh, M., Bechikh, S., Hung, C.-C., & Ben Said, L. (2019). Multi-objective evolution of oblique decision trees for imbalanced data binary classification. Swarm and Evolutionary Computation, 49, 1–22. https://doi.org/10.1016/j.swevo.2019.05.005
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Dao, D. V., Adeli, H., Ly, H.-B., Le, L. M., Le, V. M., Le, T.-T., & Pham, B. T. (2020). A sensitivity and robustness analysis of GPR and ann for high-performance concrete compressive strength prediction using a Monte Carlo Simulation. Sustainability, 12(3), 830. https://doi.org/10.3390/su12030830
De Luca, F., Migliori, S., Muhammad, H., & Rapposelli, A. (2022). Corporate board and firm performance: A data envelopment analysis (DEA) of Italian listed companies. https://virtusinterpress.org/spip.php?action=telecharger&arg=10397&hash=fb14c0f77e2c59eb30bd4702351e6c8595e33706
Fabianová, J., Janeková, J., Fedorko, G., & Molnár, V. (2023). A comprehensive methodology for investment project assessment based on Monte Carlo simulation. https://www.mdpi.com/2076-3417/13/10/6103/pdf?version=1684233332
Kadkhodaei, M. H., Ghasemi, E., & Sari, M. (2022). Stochastic assessment of rockburst potential in underground spaces using Monte Carlo Simulation. Environmental Earth Sciences, 81(18). https://doi.org/10.1007/s12665-022-10561-z
Kaffash, S., Azizi, R., Huang, Y., & Zhu, J. (2020). A survey of data envelopment analysis applications in the insurance industry 1993–2018. European Journal of Operational Research, 284(3), 801–813. https://doi.org/10.1016/j.ejor.2019.07.034
Kori, G. S., & Kakkasageri, M. S. (2023). Classification and regression tree (CART) based Resource Allocation Scheme for wireless sensor networks. Computer Communications, 197, 242–254. https://doi.org/10.1016/j.comcom.2022.11.003
Li, S., & Tsai, S. (2018). Efficiency of apparel retail at the firm level-- an evaluation using data envelopment analysis (DEA). https://medcraveonline.com/JTEFT/JTEFT-04-00130.pdf
Lokesh, Y. (2022). Investigating the Monte Carlo Simulation Method to Assess and Quantify Risk and Uncertainty. International Journal of Multidisciplinary Innovative Research - ijmir.org. https://ijmir.org/doc/archive/special_issue/2022%20Oct’%20Special%20Issue.pdf
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Masih, J., Rajasekaran, R., Saini, N., & Kaur, D. (2021). Comparative analysis of machine learning algorithms for stock market prediction during COVID-19 Outbreak. Artificial Intelligence Systems and the Internet of Things in the Digital Era, 154–161. https://doi.org/10.1007/978-3-030-77246-8_15
Menges, F., Latzo, T., Vielberth, M., Sobola, S., Pöhls, H. C., Taubmann, B., Köstler, J., Puchta, A., Freiling, F., Reiser, H. P., & Pernul, G. (2021). Towards GDPR-compliant data processing in modern SIEM systems. Computers & Security, 103, 102165. https://doi.org/10.1016/j.cose.2020.102165
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Appendices

Speaker Notes

Slide 1: Title Slide
"Good afternoon everyone, I’m [Your Full Name], and today I’ll be presenting a comprehensive portfolio of analytics methods used in accounting, finance, and economics."
Slide 2: Summary Slide
"We will explore five distinct analytics methods, each with its unique application in the financial sector – these methods are instrumental in forecasting, risk assessment, efficiency measurement, and predictive modeling."
Slide 3: Time Series Analysis
"Starting with Time Series Analysis, this method analyzes time-ordered data points to identify trends and patterns. It’s particularly vital in forecasting stock prices by studying historical data and predicting future market trends."
Slide 4: Application of Time Series Analysis
"Time Series Analysis is integral for investors. By predicting stock prices of companies like Apple and Amazon, it enables informed investment decisions and optimal portfolio management."
Slide 5: Regression Analysis
"Next, we have Regression Analysis. It’s a statistical approach used to understand the relationships among variables, essential for modeling the impact of interest rate variations on investment spending."
Slide 6: Application of Regression Analysis
"Central banks and policymakers leverage Regression Analysis to understand how changes in interest rates influence economic activity, thereby shaping economic policy."
Slide 7: Monte Carlo Simulation
"Moving on to Monte Carlo Simulation, this computational technique uses randomness to assess risk by simulating multiple scenarios and calculating the probability distribution of outcomes."
Slide 8: Application of Monte Carlo Simulation
"Investors utilize this method to evaluate risk in investment portfolios under various market conditions, aiding in decision-making and portfolio optimization."
Slide 9: Data Envelopment Analysis (DEA)
"Data Envelopment Analysis, or DEA, measures production efficiency using multiple inputs and outputs. It evaluates the relative efficiency of firms and identifies areas for improvement."
Slide 10: Application of Data Envelopment Analysis
"DEA is employed to compare firms' efficiency, facilitating benchmarking and performance improvement. For instance, regulatory agencies might use DEA to assess the efficiency of utility companies."
Slide 11: Machine Learning – Decision Trees
"Next, we delve into Machine Learning, focusing on Decision Trees. These algorithms are used for classification and regression, aiding in predicting outcomes such as bankruptcy."
Slide 12: Application of Decision Trees
"Financial institutions use Decision Trees to assess the likelihood of bankruptcy, enabling them to identify high-risk entities and make informed lending and investment decisions."
Slide 13 & 14: Monte Carlo Simulation – Detailed Investigation
Speaker Notes:
"Let’s delve deeper into Monte Carlo Simulation. This method is a cornerstone in financial engineering, used extensively for risk management, option pricing, and portfolio management. It allows for extensive scenario analysis and portfolio optimization."
Slide 15: Application of Monte Carlo Simulation – In Depth
"In practical terms, hedge funds and asset managers routinely employ this simulation for strategic asset allocation and managing risk, emphasizing its real-world applicability."
Slide 16: Reflection on Limitations
"While these methods are invaluable, they come with limitations. For instance, Time Series Analysis is sensitive to historical data assumptions, and Monte Carlo Simulation requires high-quality input data."
Slide 17 & 18: Ethical, Legal, and Privacy Issues
"Lastly, addressing ethical, legal, and privacy considerations is paramount. Ensuring data privacy, mitigating biases, maintaining transparency, and adhering to regulations are essential aspects of responsible analytics."
Conclusion (at the end of Slide 18)
"In conclusion, analytics methods play a pivotal role in accounting, finance, and economics, enabling forecasting, risk assessment, and decision-making. However, it’s crucial to acknowledge their limitations and navigate the ethical and legal landscape diligently."

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