Unconventional Applications of Financial Analysis Techniques

Financial analysis and modelling are becoming increasingly relevant in modern finance. There is strong need for greater flexibility and wider coverage in the applications of trading, asset pricing and risk management, highlighted by the systemic collapse of financial institutions during the global financial crisis of 2007–08. Financial institutions that had aimed for profit maximisation now felt the need to constrain growth and use better modelling and risk management tools. There is also a sharp increase in the need to conceive and apply these models and tools in an innovative manner. This thesis presents studies in three different categories of analysis: the study of technical analysis and its use as an indicator for market risk and efficiency; the application of survivorship bias in a post-financial crisis environment and its effects on risk–reward structures of short to long term investment; and the opportunity for arbitrage by taking advantage of regulatory restrictions such as circuit breakers and price limits. This thesis studies three markets, the Australian, US, and Chinese Equity markets across three investigations respectively.  The first study explores a new explanation of why technical analysis still prevails despite evidence against it in the form of market efficiency. Rational investors should use technical analysis to benefit themselves. This study postulates that if abnormal excess return cannot be consistently generated, investors use technical analysis to reduce transaction costs and overall risk of trade. Connections can be drawn by exploring the links between common technical indicators and market efficiency proxies such as spread, liquidity and order book depth. The spread measures the implicit transaction cost as well as being an indicator of relative market efficiency. Market liquidity provides insight into how investors with large amounts of capital can potentially work their orders to minimise slippage. Order book depth explores the level of potential slippage relative to trading size experienced by these investors when choosing to operate with technical analysis as their trading signal.  The second study describes the application of the Black–Litterman model in a post-crisis scenario. In this scenario, general parametrics should fail due to irrational fear and overshooting of investor expectations. During these stressful times in the market, surviving firms are found to be financially sound or are saved via government or central bank interventions. These overreactions from the investors should be proxied by the return distribution of individual equities around the return of the market index (S&P500). Overperforming stocks should fare better in the medium to long run as investors are confident about these firms even during turmoil. Underperforming stocks should perform better in the short run as the market compensates for its overreaction once investors realise that these firms are cheap on a fundamental basis. Only the top and bottom quartiles are considered for view adjustments in the Black–Litterman model as these have the most significant shifts during the fall. In the final analysis, firm size and book to market ratios are controlled in a similar fashion as the Fama French three-factor model.  The last study investigates arbitrage opportunities in China. By taking advantage of Chinese circuit breaker regulations in the form of price limits on the Shanghai and Shenzhen Stock Exchanges, mispricing opportunities can be exploited via the convertible bonds market. Hedging exposures using the convertible bonds against their underlying equity when mispricing occurs demonstrates a significant return above the risk-free rate (10-year Chinese government bond yield) in an empirical context between 2010 and 2019.  These three studies show that non-traditional techniques and methods should be expanded in use, especially in the field of trading and risk management. There is strong evidence that technical analysis coincides with large increases in liquidity, and momentary increase in the level of market efficiency, hence reducing transaction costs and overall idiosyncratic risk when trading in equities. Adjusting weights on a market portfolio using the Black–Litterman model can yield substantially higher returns for lower downside risk during a post-financial crisis context based on the performance of sector stocks during the crash. Finally, significant returns  above the risk-free rate can be obtained by arbitrageurs by taking advantage of regulatory inefficiencies in the Chinese market. Overall, these findings contribute towards the study of risk and return in the context of market microstructure, behavioural bias and modelling, and arbitrage via regulatory inefficiency.