A Reflection of Lessons from Trading Simulations

A Reflection of Lessons from Trading Simulations

Introduction

• Provide a key brief of what the trading simulation was all about.
• Define trading simulations and importance to business-related professionals (Zhang, Zohren, & Roberts, 2020).
• Structure of the report

Data Analysis

• Analyze data from your own trading history
• Give a brief history of what you did and how you did it
• What informed your key decisions?
• What criteria did you make in decision making?
• Would you change anything now that you have gained experience on key trading strategies?
• What literature did you use to inform some of your decisions?
• Summary of the key takeaways from individual trading history

Demonstrate an Understanding of Trading Strategies

• Define trading strategies
• Mention types of trading strategies
• Which key trading strategies do you regard as best practices and why?
• Considerations of relevant trading issues (such as, but not limited to, profits/loss, risk, etc).
• Literature review (synthesise and blend Material) on key and relevant trading issues
• Key
• Congruence with your own experience

Linking Personal Observations with Theory and Empirical Evidence

• Why simulations vary
• Behavioral aspects of trading
• Profitability: What is it and how does it impact trading?
• Risk: What is it and how does it impact trading?
• Liquidity: What is it and how does it impact trading?
• Price Impact: What is it and how does it impact trading?
• What are you personal definitions of the above and how did they impact your simulation exercises?

Limitations of Simulation

• Acknowledge key limitations of your simulation and analysis
• How did they impact the entire exercise?

Literature Review

Introduction

• Brief overview of liquidity risk
• Structure of the literature review

What is Liquidity Risk?

• Provide definition from the perspective of various scholars
• Funding liquidity risk
• Market liquidity risk

Issues in Liquidity Risk and Crisis

• A liquidity crisis occurs when demand for liquidity rises while supply falls across a large number of financial institutions or other enterprises. (Discuss)
• Widespread maturity mismatches across banks and other enterprises are at the basis of a liquidity crisis, resulting in a scarcity of cash and other liquid assets when they are required. (Discuss)
• Large, negative economic shocks or typical cyclical fluctuations in the economy may both generate liquidity crises. (Discuss)

Sources of Liquidity Risk

• Lack of Cash Flow Management. Cash flow management gives a business good visibility into potential liquidity challenges and opportunities. …
• Inability to Obtain Financing.
• Unexpected Economic Disruption.
• Unplanned Capital Expenditures.
• Profit Crisis.

Causes of Liquidity Issues

• Major causes according to extant literature

Liquidity risk management 

• Ways to manage liquidity risk

Key Takeaways From Review of Literature

• Liquidity defined.
• Liquidity risk categories
• Rudimentary indicator of liquidity

 

 

References

• Abdella, J., & Shuaib, K. (2018). Peer to peer distributed energy trading in smart grids: A survey. Energies, 11(6), 1560.
• Abouloula, K., Habil, B. E., & Krit, S. D. (2018). Money management limits to trade by robot trader for automatic trading. International Journal of Engineering, Science and Mathematics, 7(3), 195-205.
• Kyriazis, N. A. (2019). A survey on efficiency and profitable trading opportunities in cryptocurrency markets. Journal of Risk and Financial Management, 12(2), 67.
• Rundo, F. (2019). Deep LSTM with reinforcement learning layer for financial trend prediction in FX high frequency trading systems. Applied Sciences, 9(20), 4460.
• Tushar, W., Saha, T. K., Yuen, C., Smith, D., & Poor, H. V. (2020). Peer-to-peer trading in electricity networks: An overview. IEEE Transactions on Smart Grid, 11(4), 3185-3200.
• Zhang, Z., Zohren, S., & Roberts, S. (2020). Deep reinforcement learning for trading. The Journal of Financial Data Science, 2(2), 25-40.
• Zhou, Y., Wu, J., Long, C., & Ming, W. (2020). State-of-the-art analysis and perspectives for peer-to-peer energy trading. Engineering, 6(7), 739-753.
• Tavana, M., Abtahi, A. R., Di Caprio, D., & Poortarigh, M. (2018). An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking. Neurocomputing, 275, 2525-2554.
• Febi, W., Schäfer, D., Stephan, A., & Sun, C. (2018). The impact of liquidity risk on the yield spread of green bonds. Finance Research Letters, 27, 53-59.
• Ahamed, F. (2021). Determinants of Liquidity Risk in the Commercial Banks in Bangladesh. European Journal of Business and Management Research, 6(1), 164-169.
• Mohammad, S., Asutay, M., Dixon, R., & Platonova, E. (2020). Liquidity risk exposure and its determinants in the banking sector: A comparative analysis between Islamic, conventional and hybrid banks. Journal of International Financial Markets, Institutions and Money, 66, 101196.
• Goodhart, C. (2008). Liquidity risk management. Banque de France Financial Stability Review, 11, 39-44.
• Brunnermeier, M. K., & Yogo, M. (2009). A note on liquidity risk management. American Economic Review, 99(2), 578-83.
• Cornett, M. M., McNutt, J. J., Strahan, P. E., & Tehranian, H. (2011). Liquidity risk management and credit supply in the financial crisis. Journal of financial economics, 101(2), 297-312.
• Vento, G. A., & La Ganga, P. (2009). Bank liquidity risk management and supervision: which lessons from recent market turmoil. Journal of Money, Investment and Banking, 10(10), 78-125.

 

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