Trading strategy

Summary

In finance, a trading strategy is a fixed plan that is designed to achieve a profitable return by going long or short in markets. The main reasons that a properly researched trading strategy helps are its verifiability, quantifiability, consistency, and objectivity. For every trading strategy one needs to define assets to trade, entry/exit points and money management rules. Bad money management can make a potentially profitable strategy unprofitable. Trading strategies are based on fundamental or technical analysis, or both. They are usually verified by backtesting, where the process should follow the scientific method, and by forward testing (a.k.a. 'paper trading') where they are tested in a simulated trading environment. Types of trading strategies The term trading strategy can in brief be used by any fixed plan of trading a financial instrument, but the general use of the term is within computer assisted trading, where a trading strategy is implemented as computer prog

Official source

https://en.wikipedia.org/wiki/Trading_strategy

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The objective of this course is to provide a detailed coverage of the standard models for the valuation and hedging of derivatives products such as European options, American options, forward contracts, futures contract and exotic options.

This course provides an overview of the theory of asset pricing and portfolio choice theory following historical developments in the field and putting emphasis on theoretical models that help our understanding of financial decision making and financial markets.

The course covers a wide range of topics in investment analysis

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Bartering Books to Beers: a Recommender System for Exchange Platforms

Michele Catasta, Jérémie Rappaz, Maria-Luiza Vladarean

Bartering is a timeless practice that is becoming increasingly popular on the Web. Recommending trades for an online bartering platform shares many similarities with traditional approaches to recommendation, in particular the need to model the preferences of users and the properties of the items they consume. However, there are several aspects that make bartering problems interesting and challenging, specifically the fact that users are both suppliers and consumers, and that the trading environment is highly dynamic. Thus, a successful model of bartering requires us to understand not just users’ preferences, but also the social dynamics of who trades with whom, and the temporal dynamics of when trades occur. We propose new models for bartering-based recommendation, for which we introduce three novel datasets from online bartering platforms. Surprisingly, we find that existing methods (based on matching algorithms) perform poorly on real-world platforms, as they rely on idealized assumptions that are not supported by real bartering data. We develop approaches based on Matrix Factorization in order to model the reciprocal interest between users and each other’s items. We also find that the social ties between members have a strong influence, as does the time at which they trade, therefore we extend our model to be socially- and temporally- aware. We evaluate our approach on trades covering books, video games, and beers, where we obtain promising empirical performance compared to existing techniques.

2017

Mutual fund portfolio choice in the presence of dynamic ﬂows

Julien Hugonnier

We analyze the implications of dynamic flows on a mutual fund's portfolio decisions. In our model, myopic investors dynamically allocate capital between a riskless asset and an actively managed fund which charges fraction-of-fund fees. The presence of dynamic flows induces "flow hedging" portfolio distortions on the part of the fund, even though investors are myopic. Our model predicts a positive relationship between a fund's proportional fee rate and its volatility. This is a consequence of higher-fee funds holding more extreme equity positions. Although both the fund portfolio and investors' trading strategies depend on the proportional fee rate, the equilibrium value functions do not. Finally, we show that our results hold even if investors are allowed to directly trade some of the risky securities.

2010

Exploring the Nature of "Trader Intuition"

Experimental evidence has consistently confirmed the ability of uninformed traders, even novices, to infer information from the trading process. After contrasting brain activation in subjects watching markets with and without insiders, we hypothesize that Theory of Mind (ToM) helps explain this pattern, where ToM refers to the human capacity to discern malicious or benevolent intent. We find that skill in predicting price changes in markets with insiders correlates with scores on two ToM tests. We document GARCH-like persistence in transaction price changes that may help investors read markets when there are insiders.

2010