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Tonight, I started programming a knowledge base in the Prolog computer language used in artificial intelligence. This will also form part of how I map out the underlying logics of two PhD case studies on strategic culture before I get to specific research methods process tracing for one case study and thematic coding analysis for the other.
This is like a battle between a discredited, naive form of active management and low-fee passive management that disrupts mutual funds. But there are at least two other options found more in funds management: The first is a goal of activist hedge funds who use behavioural finance to create situations of crowded trades and rational herding; the second informs quantitative trading techniques like machine learning and statistical arbitrage. The first leaks to the media; the second uses news and text analytics to anticipate microstructure changes in asset prices.
I spent a decade studying the first in media analysis, publishing and research. Now, I am interested in the quantitative insights of the second.
This feels like a self-disruption of past work. System designers combine both options mentioned above in funds management: Then, I will start on the black box version with live data. I have access to live data now — but I need more of a background in algorithms, machine learning, stream processing, and high-frequency econometrics — to really begin work on the black box version.
The following themes emerged from the reading list, and from also checking the rankings of several hundred books at Amazon. Bayesian probabilities; investor psychology; market microstructure; and risk management models such as Monte Carlo simulation, Value at Risk, and systematic risk. This thematic analysis will help to focus my post-PhD research on the sociology of finance into the following initial research questions:. The Free Press, Risk free option trading using arbitrage richards bay specific risk exposures might these multi-assets face, and under what conditions?
McGraw-Hill, is the classic book on institutional portfolio models. Academic Press, is a recent book I will look at. Theory, Evidence, and Policy New York: Oxford University Press, deal respectively with the practice and theory of contemporary financial markets. There are many books on behavioural finance and investor psychology: How can algorithmic trading and computational techniques model the risk-return dynamics of alpha generation?
Despite its flaws Rishi K. Cambridge University Press, Academic Press, deal with order types in algorithmic trading. Theory and Practice New York: The dominance of bank proprietary trading desks explains several aspects that Chan had omitted from his description of intraday trading strategies. Chan and others relied on contracts for difference without overnight holdings.
Their tenants include banking, financial services, investment banking, investment brokerage, and private equity firms. Much of this is outdated information from an institutional banking perspective which relies on non-public trade secrets. Penguin, as a reminder of the tacit knowledge that a trader may create through personal experience, research, and reflection.
Several days later I learned of a new University of Toronto study PDF on how retail traders and high-frequency traders interacted on the Toronto Stock Exchange in The study felt like a research counterpoint to the Lewis book.
The study found that retail investors largely benefited from the market microstructure of high-frequency trading firms. To develop a greater awareness of how bank proprietary trading desks affect market microstructure using dominant trading strategies in a predator-prey ecosystem. Over the past few years I have investigated facets of HFT. It covers an historical overview; some relevant theory; and the use of computer algorithms and machine learning.
Large-scale HFT firms spend millions on their computing and technological infrastructure. The introductory reading list hopefully shows how you can use research skills to Understand a media debate or knowledge domain in greater detail. A history of algorithmic and high-frequency trading on Wall Street, and the emergence of dark pools.
Inside The Black Box: An introduction to quantitative trading models and coverage of the media debate about high-frequency trading. Oxford University Press, Hasbrouck explains the empirical approaches to market microstructure that underpin high-frequency trading. The current debates on how high-frequency trading has affected liquidity and price discovery in markets, and the growth of market microstructure frameworks.
An introduction to Bayesian probability and data analysis using filters and machine learning. Cambridge University Press, TS An advanced overview of high-frequency data and relevant econometric models for liquidity, volatility, and market microstructure analysis. Mariani, and Ionut Florescu New York: An advanced reference on how to model high-frequency data. An advanced introduction to how algorithmic trading influences market microstructure, and is used for the transaction and execution systems of high-frequency trading.
Insights from mathematics and computer science about how to develop, test, and automate the algorithmic trading strategies, using agent-based learning. The authors developed the TSSB software program that uses machine learning to implement algorithmic trading strategies. Risk free option trading using arbitrage richards bay analysis TA is the study of group psychology in financial market using price, sentiment, and volume indicators, and pattern recognition. It risk free option trading using arbitrage richards bay in a modern context due to Charles H.
TA focuses on identification of trends, retracements, breakouts, pullbacks, support and resistance. It anticipated some aspects of current academic research programs on behavioural finance and market microstructure but from a trader or practitioner viewpoint.
Early studies from to by Eugene Fama and his University of Chicago colleagues found that TA filter rules were unprofitable once transaction and risk free option trading using arbitrage richards bay costs were considered. In contrast, TA became popular in the mid-late s amongst trend-following Commodity Trading Advisors on volatile commodities and foreign exchange markets. Finance theories in academic journals and hedge fund manager practices diverged into parallel universes.
Recent academic research has shed new light on this academic-practitioner divide. This finding reflects the period when Sperandeo, Jones, Borish, and other non-TA traders like Martin Zweig were ascendant in financial markets. It contradicts the earlier findings of Cowles and Fama that TA has always been unprofitable.
These find that the traders used arbitrage on anomalies; the transmission shocks of central bank monetary policies; the anchoring, crowded exits and rational herding of institutional investors; and changes to the international monetary system and political economy. Kindleberger, John Kenneth Galbraith, and Hyman Minsky—which has inspired contemporary research in behavioural finance.
Money Never Sleeps had pictures from the Dutch Tulip bubble The conceptual gap between TA and behavioural finance is perhaps not as large for financial market practitioners as some academic researchers believe. The decline in TA profitability after the early s can be attributed to changes in central bank policy coordination, market microstructure, and the growth of algorithmic trading.
But the growth of new trading—options, futures, and high-frequency systems—have altered what the Wyckoff Method found in pre-World War II financial markets. Collectively, the above developments over the past two decades have changed markets and volatility from trending to more range-bound dynamics.
This Darwinian-like evolution has led to the demise of dotcom era day tradersand trend followers who benefited from asset price valuations due to housing and commodities speculative bubbles Academic researchers rarely refer to the TA risk free option trading using arbitrage richards bay literature beyond introductory books by Alexander Elder, Van Tharp, and other authors.
Academics often state incorrectly that TA remains unstructured as a knowledge domain: Instead, TA now involves an industry of books, consultants and custom indicators targeted at the retail investor. University of Queensland sociologist Margery Mayall found that TA indicators shaped the self-beliefs, mindsets, and decisions of the Australian retail traders who she interviewed. In contrast, proprietary trading desks now combine TA with behavioural finance, game theory, and market microstructure.
There is always someone else on the other side of the trade even if it is a market-making algorithm. Academic researchers could bridge the gap with TA practitioners if the popular models were evaluated and back-tested in a more rigorous manner. However, recent work by Andrew Lo and other authors on rehabilitating TA remains at the interview or memoir stage, rather than using a robust empirical research design.
Recent TA practitioner work by Adam H. Grimes, Xin Xie, Charles D. Kirkpatrick II, Julie R. Aronson, and others looks promising. This augments earlier work by the late Ari Kiev, Brett N. Steenbarger, and Mark Douglas on trading and performance psychology. Since circaa subset of TA academic research has also used genetic algorithms and high-frequency tick data analysis to identify trading rules. The findings from this research often either remain proprietary or reflect mathematical and quantitative models.
Hedge fund managers who use TA are closer to Risk free option trading using arbitrage richards bay C. Such hedge fund managers are often aware of confirmation bias, the disposition effect, overconfidence, model risk free option trading using arbitrage richards bay, and risk free option trading using arbitrage richards bay cognitive biases identified in the behavioural finance literature.
Hedge fund managers and professional traders now use TA in a mixed methods approach — if they have not already been replaced by algorithmic trading systems. Another problem with the genetic algorithms research is that whilst it identifies trading rules it often does not include trader learning, risk and money management practices.
These are what Sperandeo, Jones II, Borish and other TA traders use, and thus these practices modify the efficacy of the trading rules identified. Academic researchers using genetic algorithms and other methods have often overlooked this cunning or metic intelligence.
Academic research rigour can be combined with professional trading insights whilst retail trading myths promulgated by the TA industry and self-styled trading coaches can be avoided.
A mixed methods research approach looks promising: All three approaches look at the same market data via different lenses and vantage points. I took several MarketPsych. Once identified, I risk free option trading using arbitrage richards bay compared the personal cognitive biases with past trades using an after action review approach.