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

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  • 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. Energies11(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 Mathematics7(3), 195-205.
  • Kyriazis, N. A. (2019). A survey on efficiency and profitable trading opportunities in cryptocurrency markets. Journal of Risk and Financial Management12(2), 67.
  • Rundo, F. (2019). Deep LSTM with reinforcement learning layer for financial trend prediction in FX high frequency trading systems. Applied Sciences9(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 Grid11(4), 3185-3200.
  • Zhang, Z., Zohren, S., & Roberts, S. (2020). Deep reinforcement learning for trading. The Journal of Financial Data Science2(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. Engineering6(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. Neurocomputing275, 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 Letters27, 53-59.
  • Ahamed, F. (2021). Determinants of Liquidity Risk in the Commercial Banks in Bangladesh. European Journal of Business and Management Research6(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 Money66, 101196.
  • Goodhart, C. (2008). Liquidity risk management. Banque de France Financial Stability Review11, 39-44.
  • Brunnermeier, M. K., & Yogo, M. (2009). A note on liquidity risk management. American Economic Review99(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 economics101(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 Banking10(10), 78-125.

 

 
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