Portfolio Optimization & Asset Allocation

The explosive development of electronic media has brought to the market participants thousands of pieces of financial news which are released on different platforms every day. Many news wires published online are editorially controlled and can be relied as factual summary as opposed to fake news or disinformation. These news
items provide a rich source of textual information which in a summative way represents the sentiment of the market. The sentiments influence or impact the asset price as well as the volatility of individual assets. In this study we have tested sentiment enhanced daily trading strategies. Alexandria Technology has provided us news sentiment metadata, which is used in this study. We have also resorted to ‘asset filters’ which we use to restrict the universe of assets chosen for daily trades. We have considered quantified news sentiment and its impact on the movement of asset prices as a second time series data, which is used together with the asset price/return time series data. Our asset allocation strategy uses Second Order Stochastic Dominance (SSD); see Roman et al. (2006, 2013). Following this modelling paradigm we compute daily trade schedules using a time series of historical equity price data. In contrast to classical mean-variance method this approach improves the tail risk as well as the upside of the return. In our recent research we have introduced news sentiment indicators such as News RSI (NRSI) and Derived RSI (DRSI) filters. These filters restrict the choice of asset universe for trading. Consistent performance improvement achieved in back-testing vindicates our approach.

Keywords— Trading Strategy, Sentiment Analysis, News Meta Data, Asset Filter

We investigate how “news sentiment” in general and the “impact of news” in particular can be utilised in designing equity trading strategies. News is an event that moves the market in a small way or a big way. We have introduced a derived measure of news impact score which takes into consideration news flow and decay of sentiment. Since asset behaviour is characterised by return, volatility and liquidity we first consider a predictive analytic model in which market data and impact scores are the inputs and also the independent variables of the model. We finally describe the trading strategies which take into consideration the three important characteristics of an asset, namely, return, volatility and liquidity. The minute-bar market data as well as intraday news sentiment metadata have been provided by Thomson Reuters.

The explosive development of electronic media has brought to the market participants thousands of pieces of financial news which are released on different platforms every day. Many news wires published online are editorially controlled and can be relied as factual summary as opposed to fake news or disinformation. These news items provide a rich source of textual information which in a summative way represents the sentiment of the market. The sentiments influence or impact the asset price as well as the volatility of individual assets. In this study we have tested sentiment enhanced daily trading strategies. Alexandria Technology has provided us news sentiment metadata, which is used in this study. We have also resorted to ‘asset filters’ which we use to restrict the universe of assets chosen for daily trades. We have considered quantified news sentiment and its impact on the movement of asset prices as a second time series data, which is used together with the asset price/return time series data. Our asset allocation strategy uses Second Order Stochastic Dominance (SSD); see Roman et al. (2006, 2013). Following this modelling paradigm we compute daily trade schedules using a time series of historical equity price data. In contrast to classical mean-variance method this approach improves the tail risk as well as the upside of the return. In our recent research we have introduced news sentiment indicators such as News RSI (NRSI) and Derived RSI (DRSI) filters. These filters restrict the choice of asset universe for trading. Consistent performance improvement achieved in back-testing vindicates our approach.

Key Words: Trading Strategy, Sentiment Analysis, News Meta Data, Asset Filter

The rapid rise of social media communication has touched upon all aspects of our social and commercial life. In particular, the rise of social media as the most preferred way of connecting people on-line has led to new models of information communication amongst the peers. Of these media Twitter has emerged as a particularly strong platform and in the financial domain tweets by market participants are of great interest and value. News in general, and commercial and financial news wires, in particular provide the market sentiment and in turn influence the asset price behaviour in the financial markets. In a comparable way micro-blogs of tweets generate sentiment and has an impact on market behaviour, that is , the price as well as the volatility of stock prices.

In one of our recent research we have introduced news sentiment based filters such as News RSI (NRSI) and Derived RSI (DRSI), which restrict the choice of asset universe for trading. In this present study, we have extended the same approach to StockTwit’s data. We use the filter approach of asset selection and restrict the available asset universe. We then apply our daily trading strategy using the Second Order Stochastic Dominance (SSD) as an asset allocation model. Our trading model is instantiated by two time series data, namely, (i) historical market price data and (ii) StockTwits sentiment (scores) data. Instead of NRSI we compute the Micro-blog RSI (MRSI) and using this a DRSI is computed. The resulting combined filter (DRSI) leads to an enhancement of the SSD based trading and asset allocation strategy. Empirical experimental results of constructing portfolios are reported for S&P 500 Index constituents.

Key Words: Trading Strategy, Sentiment Analysis, Micro blog data, Asset Filter

The recommendation of financial analysts plays an important role in making investment decisions. The method of constructing a portfolio using such recommendations does not rely on quantitative models instead it relies on research of the analysts and their qualitative views. We explore paradigms of modelling whereby the qualitative research outputs of the analysts are introduced in quantitative models of portfolio construction. In this report, the quality of the analysts research is modelled using exploratory data analysis techniques. The findings are then used to create a model of filters. This method narrows down the choice of the asset universe, thereby improving the results of the asset allocation investment model.

The market data used in this report is stock prices for 83 assets, which are or once were components of the NIFTY 50 stock index. The market data and the analyst recommendations are supplied by Thomson Reuters and the study covers the period from 1 January 2013 to 25 July 2018.

Key Words: Analyst Recommendations; Filters; NIFTY50 Index

Second order Stochastic Dominance (SSD) has a well recognised importance in portfolio selection, since it provides a natural interpretation of the theory of risk-averse investor behaviour. Recently, SSD-based models of portfolio choice have been proposed; these assume that a reference distribution is available and a portfolio is constructed, whose return distribution dominates the reference distribution with respect to SSD. We present an empirical study which analyses the effectiveness of such strategies in the context of enhanced indexation.

We formulate a portfolio planning model that is based on second-order stochastic dominance as the choice criterion. This model is an enhanced version of the multi-objective model proposed by Roman et al. ; the model compares the scaled values of the different objectives, representing tails at different confidence levels of the resulting distribution. The proposed model can be formulated as a risk minimization model where the objective function is a convex risk measure; we characterize this risk measure and the resulting optimization problem. Moreover, our formulation offers a natural generalization of the SSD-constrained model of Dentcheva and Ruszczyn4 ski . A cutting plane-based solution method for the proposed model is outlined. We present a computational study showing: (a) the effectiveness of the solution methods and (b) the improved modeling capabilities: the resulting portfolios have superior return distributions.

Second-order stochastic dominance (SSD) is widely recognised as an important decision criterion in portfolio selection. Unfortunately, stochastic dominance models are known to be very demanding from a computational point of view. In this paper we consider two classes of models which use SSD as a choice criterion. The first, proposed by Dentcheva and Ruszczy4nski (J Bank Finance 30:433-451, 2006), uses a SSD constraint, which can be expressed as integrated chance constraints (ICCs). The second, proposed by Roman et al. (Math Program, Ser B 108:541-569, 2006) uses SSD through a multi-objective formulation with CVaR objectives. Cutting plane representations and algorithms were proposed by Klein Haneveld and Van der Vlerk (Comput Manage Sci 3:245-269, 2006) for ICCs, and by K|nzi-Bay and Mayer (Comput Manage Sci 3:3-27, 2006) for CVaRminimization. These concepts are taken into consideration to propose representations and solution methods for the above class of SSD based models.We describe a cutting plane based solution algorithm and outline implementation details. A computational study is presented, which demonstrates the effectiveness and the scale-up properties of the solution algorithm, as applied to the SSD model of Roman et al. (Math Program, Ser B 108:541-569, 2006).

This paper considers longshort portfolio optimization in the presence of two risk measures (variance and conditional value-at-risk (CVaR)), and asset choice constraints regarding buying and selling and holding thresholds, and cardinality restrictions on the number of stocks to be held in the portfolio. The mean-variance-CVaR model is based on the mean-variance approach but has an additional constraint on CVaR. Our empirical investigations show that short-selling strategies lead to a superior choice of portfolios, with higher expected return and much lower risk exposures. In particular, the downside risk can be considerably reduced by introducing short selling. Our longshort extension to the mean-variance-CVaR model incorporates the practice of many financial institutions with regard to short decisions. Numerical experiments with the resulting model, which is a quadratic mixed integer program, are conducted on real data drawn from the FTSE 100 index

Modern Portfolio Theory (MPT) is based upon the classical Markowitz model which uses variance as a risk measure. A generalisation of this approach leads to mean-risk models, in which a return distribution is characterised by the expected value of return (desired to be large) and a “risk” value (desired to be kept small). Portfolio choice is made by solving an optimization problem, in which the portfolio risk is minimised and a desired level of expected return is specified as a constraint. The need to penalize different undesirable aspects of the return distribution led to the proposal of alternative risk measures, notably those penalising only the downside part (adverse) and not the upside (potential). The downside risk considerations constitute the basis of the Post Modern Portfolio Theory (PMPT). Examples of such risk measures are lower partial moments, Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). We revisit these risk measures and the resulting mean-risk models. We discuss alternative models for portfolio selection, their choice criteria and the evolution of MPT to PMPT which incorporates: utility maximisation and stochastic dominance.

This paper proposes a model for portfolio optimization, in which distributions are characterized and compared on the basis of three statistics: the expected value, the variance and the CVaR at a specified confidence level. The problem is multi-objective and transformed into a single objective problem in which variance is minimized while constraints are imposed on the expected value and CVaR. In the case of discrete random variables, the problem is a quadratic program. The mean-variance (mean-CVaR) efficient solutions that are not dominated with respect to CVaR (variance) are particular efficient solutions of the proposed model. In addition, the model has efficient solutions that are discarded by both mean-variance and mean-CVaR models, although they may improve the return distribution. The model is tested on real data drawn from the FTSE 100 index. An analysis of the return distribution of the chosen portfolios is presented.

Mean-risk models have been widely used in portfolio optimization. However, such models may produce portfolios that are dominated with respect to second order stochastic dominance and therefore not optimal for rational and risk-averse investors. This paper considers the problem of constructing a portfolio which is non-dominated with respect to second order stochastic dominance and whose return distribution has specified desirable properties. The problem is multi-objective and is transformed into a single objective problem by using the reference point method, in which target levels, known as aspiration points, are specified for the objective functions. A model is proposed in which the aspiration points relate to ordered outcomes for the portfolio return. This concept is extended by additionally specifying reservation points, which act pre-emptively in the optimization model. The theoretical properties of the models are studied. The performance of the models on real data drawn from the Hang Seng index is also investigated.

We consider the mean-variance (M-V) model of Markowitz and the construction of the risk-return efficient frontier. We examine the effects of applying buy-in thresholds, cardinality constraints and transaction roundlot restrictions to the portfolio selection problem. Such discrete constraints are of practical importance but make the efficient frontier discontinuous. The resulting quadratic mixed-integer (QMIP) problems are NP-hard and therefore computing the entire efficient frontier is computationally challenging. We propose alternative approaches for computing this frontier and provide insight into its discontinuous structure. Computational results are reported for a set of benchmark test problems.

This white paper introduces Markowitz mean-variance model with a general overview and sets out to explain why and how the finance industry has fully embraced this as a method of choice for portfolio planning. The main focus of the white paper is to bring out many aspects of the portfolio planning problem which are addressed by enhanced mean-variance models that meet the growing requirements of the finance industry. Portfolio analysis is a leading issue with fund managers who apply such models in many situations such as index tracking, performance evaluation and historical data/backtesting. This white paper will be of interest to: – Fund Managers – Trading Desk Staff – Back Office Staff – Quantitative Analysts who wish to know the general development in the market place.