Measuring the Behavioural Component of the S&P 500 and its Relationship to Financial Stress and Aggregated Earnings Surprises

Scholars in management and economics have shown increasing interest in isolating the behavioural dimension of market evolution. Indeed, by improving forecast accuracy and precision, this exercise would certainly help firms to anticipate economic fluctuations, thus leading to more profitable business and investment strategies. Yet, how to extract the behavioural component from real market data remains an open question. By using monthly data on the returns of the constituents of the S&P 500 index, we propose a Bayesian methodology to measure the extent to which market data conform to what is predicted by prospect theory (the behavioural perspective), relative to the (standard) subjective expected utility theory baseline.We document a significant behavioural component that reaches its peaks during recession periods and is correlated to measures of financial volatility, market sentiment and financial stress with expected sign. Moreover, the behavioural component decreases around macroeconomic corporate earnings news, while it reacts positively to the number of surprising announcements

Coherent Asset Allocation and Diversification in the Presence of Stress Events

We propose a method to integrate frequentist and subjective probabilities in order to obtain a coherent asset allocation in the presence of stress events. Our working assumption is that in normal market asset returns are sufficiently regular for frequentist statistical techniques to identify their joint distribution, once the outliers have been removed from the data set. We also argue, however, that the exceptional events facing the portfolio manager at any point in time are specific to the each individual crisis, and that past regularities cannot be relied upon. We therefore deal with exceptional returns by eliciting subjective probabilities, and by employing the Bayesian net technology to ensure logical consistency. The portfolio allocation is then obtained by utility maximization over the combined (normal plus exceptional) distribution of returns. We show the procedure in detail in a stylized case.Stress tests, asset allocation, Bayesian Networks

Views, Factor Models and Optimal Asset Allocation

AbstractMaking investment decisions in general is a decision-making problem under uncertainty. How well an actual investment portfolio performs depends on the future evolution of economic and financial variables such as interest rates, asset returns and inflation rates. The future evolution of these risk drivers is traditionally modelled using time series models, and it is assumed that historical data are relevant for assessing future risk and return. However, opinions vary about the extent to which all forward-looking information can be derived from historical data. Consequently, a framework for combining views and model-based (density) forecasts is indispensable. We present a concrete example of how views can consistently be combined with model-based (density) forecasts and how this affects investment decisions

Asset liability modelling and pension schemes: the application of robust optimization to USS

This paper uses a novel numerical optimization technique - robust optimization - that is well suited to solving the asset-liability management (ALM) problem for pension schemes. It requires the estimation of fewer stochastic parameters, reduces estimation risk and adopts a prudent approach to asset allocation. This study is the first to apply it to a real-world pension scheme, and the first ALM model of a pension scheme to maximise the Sharpe ratio. We disaggregate pension liabilities into three components - active members, deferred members and pensioners, and transform the optimal asset allocation into the scheme’s projected contribution rate. The robust optimization model is extended to include liabilities and used to derive optimal investment policies for the Universities Superannuation Scheme (USS), benchmarked against the Sharpe and Tint, Bayes-Stein, and Black-Litterman models as well as the actual USS investment decisions. Over a 144 month out-of-sample period robust optimization is superior to the four benchmarks across 20 performance criteria, and has a remarkably stable asset allocation – essentially fix-mix. These conclusions are supported by six robustness checks

Survey of quantitative investment strategies in the Russian stock market : Special interest in tactical asset allocation

Russia’s financial markets have been an uncharted area when it comes to exploring the performance of investment strategies based on modern portfolio theory. In this thesis, we focus on the country’s stock market and study whether profitable investments can be made while at the same time taking uncertainties, risks, and dependencies into account. We also pay particular interest in tactical asset allocation. The benefit of this approach is that we can utilize time series forecasting methods to produce trading signals in addition to optimization methods. We use two datasets in our empirical applications. The first one consists of nine sectoral indices covering the period from 2008 to 2017, and the other includes altogether 42 stocks listed on the Moscow Exchange covering the years 2011 – 2017. The strategies considered have been divided into five sections. In the first part, we study classical and robust mean-risk portfolios and the modeling of transaction costs. We find that the expected return should be maximized per unit expected shortfall while simultaneously requiring that each asset contributes equally to the portfolio’s tail risk. Secondly, we show that using robust covariance estimators can improve the risk-adjusted returns of minimum variance portfolios. Thirdly, we note that robust optimization techniques are best suited for conservative investors due to the low volatility allocations they produce. In the second part, we employ statistical factor models to estimate higher-order comoments and demonstrate the benefit of the proposed method in constructing risk-optimal and expected utility-maximizing portfolios. In the third part, we utilize the Almgren–Chriss framework and sort the expected returns according to the assumed momentum anomaly. We discover that this method produces stable allocations performing exceptionally well in the market upturn. In the fourth part, we show that forecasts produced by VECM and GARCH models can be used profitably in optimizations based on the Black–Litterman, copula opinion pooling, and entropy pooling models. In the final part, we develop a wealth protection strategy capable of timing market changes thanks to the return predictions based on an ARIMA model. Therefore, it can be stated that it has been possible to make safe and profitable investments in the Russian stock market even when reasonable transaction costs have been taken into account. We also argue that market inefficiencies could have been exploited by structuring statistical arbitrage and other tactical asset allocation-related strategies.Venäjän rahoitusmarkkinat ovat olleet kartoittamatonta aluetta tutkittaessa moderniin portfolioteoriaan pohjautuvien sijoitusstrategioiden käyttäytymistä. Tässä tutkielmassa keskitymme maan osakemarkkinoihin ja tarkastelemme, voidaanko taloudellisesti kannattavia sijoituksia tehdä otettaessa samalla huomioon epävarmuudet, riskit ja riippuvuudet. Kiinnitämme erityistä huomiota myös taktiseen varojen kohdentamiseen. Tämän lähestymistavan etuna on, että optimointimenetelmien lisäksi voimme hyödyntää aikasarjaennustamisen menetelmiä kaupankäyntisignaalien tuottamiseksi. Empiirisissä sovelluksissa käytämme kahta data-aineistoa. Ensimmäinen koostuu yhdeksästä teollisuusindeksistä kattaen ajanjakson 2008–2017, ja toinen sisältää 42 Moskovan pörssiin listattua osaketta kattaen vuodet 2011–2017. Tarkasteltavat strategiat on puolestaan jaoteltu viiteen osioon. Ensimmäisessä osassa tarkastelemme klassisia ja robusteja riski-tuotto -portfolioita sekä kaupankäyntikustannusten mallintamista. Havaitsemme, että odotettua tuottoa on syytä maksimoida suhteessa odotettuun vajeeseen edellyttäen samalla, että jokainen osake lisää sijoitussalkun häntäriskiä yhtä suurella osuudella. Toiseksi osoitamme, että minimivarianssiportfolioiden riskikorjattuja tuottoja voidaan parantaa robusteilla kovarianssiestimaattoreilla. Kolmanneksi toteamme robustien optimointitekniikoiden soveltuvan parhaiten konservatiivisille sijoittajille niiden tuottamien matalan volatiliteetin allokaatioiden ansiosta. Toisessa osassa hyödynnämme tilastollisia faktorimalleja korkeampien yhteismomenttien estimoinnissa ja havainnollistamme ehdotetun metodin hyödyllisyyttä riskioptimaalisten sekä odotettua hyötyä maksimoivien salkkujen rakentamisessa. Kolmannessa osassa käytämme Almgren–Chrissin viitekehystä ja asetamme odotetut tuotot suuruusjärjestykseen oletetun momentum-anomalian mukaisesti. Havaitsemme, että menetelmä tuottaa vakaita allokaatioita menestyen erityisen hyvin noususuhdanteessa. Neljännessä osassa osoitamme, että VECM- että GARCH-mallien tuottamia ennusteita voidaan hyödyntää kannattavasti niin Black–Littermanin malliin kuin kopulanäkemysten ja entropian poolaukseenkin perustuvissa optimoinneissa. Viimeisessä osassa laadimme varallisuuden suojausstrategian, joka kykenee ajoittamaan markkinoiden muutoksia ARIMA-malliin perustuvien tuottoennusteiden ansiosta. Voidaan siis todeta, että Venäjän osakemarkkinoilla on ollut mahdollista tehdä turvallisia ja tuottavia sijoituksia myös silloin kun kohtuulliset kaupankäyntikustannukset on huomioitu. Toiseksi väitämme, että markkinoiden tehottomuutta on voitu hyödyntää suunnittelemalla tilastolliseen arbitraasiin ja muihin taktiseen varojen allokointiin pohjautuvia strategioita

Optimal Reserve Holdings, Strategic Asset Allocation and Multiple-Goal Investment Plan for Sovereign Wealth Fund of China

This thesis expounds China’s foreign reserve policy and the investment management of the reserves in a behavioural approach. The research provides a behavioural explanation of China’s reserve accumulation, which is based on the optimal decision making under uncertainty. Then the thesis proposes a multiple-goal framework for strategic asset allocation of China’s reserve management and for the investment decision of Chinese Sovereign Wealth Fund (SWF). The research first tackles the reserve accumulation puzzle in China, by incorporating loss aversion and narrow framing into the utility maximisation of the representative agent who makes the decision of wealth allocation between consumption and saving under uncertainty. Due to China’s policy maker’s subscription to promoting GDP growth as the primary political goal, it is reasonable to assume that the policy maker as a representative agent derives utility not only from consumption but also from fluctuations of the value of GDP/income. This agent evaluates the possible uninsured risk of GDP fluctuation narrowly and tends to exhibit the attribute of loss aversion relative to her growth expectation as the reference point. Under the influence of loss aversion and narrow framing, the more the policy maker cares about GDP growth, the more she needs reserve assets as a precautionary means that may provide self-insurance against uninsured income risk. Such cognitive biases enhance the agent’s precautionary motive for foreign reserves in an uncertain world, which in turn leads her to believing in an optimal level of foreign reserves that is higher than that under conventional models with rational agents. Hence, this heightens the accumulation of foreign reserves in China. Second, this thesis develops a new construction of strategic asset allocation for central banks’ management of foreign reserves by way of embedding the Black-Litterman (B-L) model into the mean variance mental accounting (MVMA) framework. While the MVMA measure suggests a multiple-objective framework that may embrace the traditional objectives of reserve management, i.e. safety, liquidity and profitability, it is based on the mean-variance approach, which suffers from profound deficiencies such as the unrealistic objective function that it relies on and the tendency that the methods are prone to undue influences of outliers. So, the B-L model is applied in this study to form forward-looking return forecasts. This method allows us to overcome the error-maximising influences of the mean-variance optimization. Furthermore, one can combine the implied equilibrium excess returns as investors’ investment views to form priors for Bayesian estimation. The optimal asset allocation then can be derived in this framework, which is applied to practical use in the context of China. The third main Chapter of this thesis concerns the investment of China’s sovereign wealth fund (SWF). The establishment of the Chinese SWF can be regarded as an optimal policy response to the changing economic conditions facing China. This fund as a special investment vehicle proves very useful for China to focus on the returns objective of managing the reserve assets, on top of the safety and liquidity objectives. This is especially important in a low yield international environment. To help achieve the yield objective, this Chapter develops further the behavioural portfolio model cum the Black-Litterman method to derive the optimal asset allocation for China’s sovereign wealth fund