Cette thèse cherche à lier les cycles économiques et la gestion de portefeuille. Le premier chapitre construit un cadre théorique entre les cycles économiques et les primes de risques. Il met en évidence l’importance des points de retournement du cycle de croissance, plus connu sous le nom d’écart de production. Les deux chapitres suivants ont pour objectif de détecter en temps réel ces points de retournement. La première approche se concentre sur une méthode non paramétrique d’apprentissage automatique simple et facilement compréhensible appelée quantification vectorielle adaptative. La seconde approche utilise des méthodes plus complexes d’apprentissage automatique, dites ensemblistes : les forêts aléatoires et le boosting. Les deux démarches permettent de créer des stratégies d’investissement performantes en temps réel. Enfin, le dernier chapitre élabore une méthode d’allocation d’actifs à partir de différents algorithmes de regroupement hiérarchique. Les résultats empiriques démontrent l’intérêt de cette tentative : les portefeuilles créés sont robustes, diversifiés et lucratifs. / A well-worked theory of macro-based investment decision is introduced. The theoretical influence of economic cycles on time-varying risk premiums is explained and exhibited. The importance of the turning points of the growth cycle, better known as the output gap, is outlined. To quickly and accurately detect economic turning points, probabilistic indicators are first created from a simple and transparent machine-learning algorithm known as Learning Vector Quantization. Those indicators are robust, interpretable and preserve economic consistency. A more complex approach is then evaluated: ensemble machine learning algorithms, referred to as random forest and as boosting, are applied. The two key features of those algorithms are their abilities to entertain a large number of predictors and to perform estimation and variable selection simultaneously. With both approaches investment strategies based on the models achieve impressive risk-adjusted returns: timing the market is thus possible. At last, exploring a new way of capital allocation, a hierarchical clustering based asset allocation method is introduced. The empirical results indicate that hierarchical clustering based portfolios are robust, truly diversified and achieve statistically better risk-adjusted performances than commonly used portfolio optimization techniques.

Each individual is both a consumer and an investor in the market. It is the common goal of every investor to achieve a high return on investment through the portfolio of profit maximization. As a result, the ratio of assets in a portfolio has become a hot topic. In China, real estate and the stock market are two main markets favoured by both individual and institutional investors. And there is a significant economic link between the two markets. Therefore, their mutual relationship and long-term and short-term causality can provide good guidance for investors. This paper studies the causality and correlation between stock trading volume and real estate trading volume in 31 provinces of mainland China. The empirical results in this paper is based on a panel data from 2000 to 2016 and divides 31 provinces into three different economic regions. The panel unit root test and the Pedroni co-integration test were carried out. The Hausman test was used to select among different estimation methods. Panel Mean Group is found the most suitable analysis method. It is found that the main industries in different provinces may affect the short-term causal relationship between real estate and the stock market. But in the long run, the causal relationship between real estate and the stock market is two-way and stable.

We develop an expanded asset evaluation model dubbed the environmental, social and governance (ESG) model, which includes a sustainability factor that accounts for the value of ecological and natural capital. We incorporate a sustainability factor into the Fama-French [2015. “A Five-Factor Asset Pricing Model.” Journal of Financial Economics 116 (1): 1–22] five-factor model plus the momentum factor. Further, we expand previous models by basing ours on microeconomic principles of value maximization and the macroeconomic principles of ecological economics. We estimate the sustainability factor premium and its factor loadings and find that following sustainable strategic management practices reduced the cost of equity by 1.6% to 2.9% per year worldwide. This implies that in 2018, sustainable strategic management practices increased world GDP by $1.3 to $2.3 trillion. Our results support previous research that there is a negative relationship between sustainability performance and the cost of capital.

京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第19953号 / 経博第540号 / 新制||経||279(附属図書館) / 33049 / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 江上 雅彦, 教授 若井 克俊, 教授 原 千秋 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DFAM

We develop an expanded asset evaluation model dubbed the environmental, social and governance (ESG) model, which includes a sustainability factor that accounts for the value of ecological and natural capital. We incorporate a sustainability factor into the Fama-French [2015. “A Five-Factor Asset Pricing Model.” Journal of Financial Economics 116 (1): 1–22] five-factor model plus the momentum factor. Further, we expand previous models by basing ours on microeconomic principles of value maximization and the macroeconomic principles of ecological economics. We estimate the sustainability factor premium and its factor loadings and find that following sustainable strategic management practices reduced the cost of equity by 1.6% to 2.9% per year worldwide. This implies that in 2018, sustainable strategic management practices increased world GDP by $1.3 to $2.3 trillion. Our results support previous research that there is a negative relationship between sustainability performance and the cost of capital.

This paper focuses on how to improve strategic asset allocation in practice. Strategic asset allocation is perhaps the most fundamental issue in portfolio management and it has been thoroughly discussed in previous research. We take our starting point in the traditional work of Markowitz within portfolio optimization. We provide a new solution of how to perform portfolio optimization in practice, or more specifically how to estimate the covariance matrix, which is needed to perform conventional portfolio optimization. Many researchers within this field have noted that the return distribution of financial assets seems to vary over time, so called regime switching, which makes it dicult to estimate the covariance matrix. We solve this problem by using a Bayesian approach for developing a Markov chain Monte Carlo algorithm that detects break points in the return distribution of financial assets, thus enabling us to improve the estimation of the covariance matrix. We find that there are two break points during the time period studied and that the main difference between the periods are that the volatility was substantially higher for all assets during the period that corresponds to the financial crisis, whereas correlations were less affected. By evaluating the performance of the algorithm we find that the algorithm can increase the Sharpe ratio of a portfolio, thus that our algorithm can improve strategic asset allocation over time. / Detta examensarbete fokuserar på hur man kan förbättra tillämpningen av strategisk tillgångsslagsallokering i praktiken. Hur man allokerar kapital mellan tillgångsslag är kanske de mest fundamentala beslutet inom kapitalförvaltning och ämnet har diskuterats grundligt i litteraturen. Vårt arbete utgår från Markowitz traditionella teorier inom portföljoptimering och utifrån dessa tar vi fram ett nytt angreppssätt för att genomföra portföljoptimering i praktiken. Mer specifikt utvecklar vi ett nytt sätt att uppskatta kovar-iansmatrisen för avkastningsfördelningen för finansiella tillgångar, något som är essentiellt för att kunna beräkna de optimala portföljvikterna enligt Markowitz. Det påstås ofta att avkastningens fördelning förändras över tid; att det sker så kallade regimskiften, vilket försvårar uppskattningen av kovariansmatrisen. Vi löser detta problem genom att använda ett Bayesiansk angreppssätt där vi utvecklar en Markov chain Monte Carlo-algoritm som upptäcker brytpunkter i avkastningsfördelningen, vilket gör att uppskattningen av kovar-iansmatrisen kan förbättras. Vi finner två brytpunkter i fördelningen under den studerade tidsperioden och den huvudsakliga skillnaden mellan de olika tidsperioderna är att volatiliten var betydligt högre för samtliga tillgångar under den tidsperiod som motsvaras av finanskrisen, medan korrelationerna mellan tillgångsslagen inte påverkades lika mycket. Genom att utvärdera hur algoritmen presterar finner vi att den ökar en portföljs Sharpe ratio och således att den kan förbättra den strategiska allokeringen mellan tillgångsslagen över tid.

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The Nuclear Waste Fund constitutes a financial system that finances future costs of the management of spent nuclear fuel as well as decommissioning of nuclear power plants. The fund invests its capital under strict rules which are stipulated in the investment policy established by the board. The policy stipulates that the fund can only invest according to certain allocation limits, and restricts it to invest solely in nominal and inflation-linked bonds issued by the Swedish state as well as treasury securities. A norm portfolio is built to compare the performance of the NWF’s investments. On average, the NWF has outperformed the norm portfolio on recent years, but it may not always have been optimal. Recent studies suggest that allocation limits should be revised over time as the return and risk parameters may change over time. This study focused on simulating three different portfolios where the allocation limits and investment options were extended to see if these extensions would outperform the norm portfolio while maintaining a set risk limit. Portfolio A consisted of OMRX REAL and OMRX TBOND indexes, Portfolio B consisted of OMRX REAL, OMRX TBOND and S&P Sweden 1+ Year Investment Grade Corporate Bond Indexes, and Portfolio C consisted of OMXR REAL, OMRX TBOND and OMXSPI indexes. The return of each portfolio for different weight distributions of the assets were simulated in MATLAB, and polynomial regression models were built in order to optimize the return as a function of the assets’ weights using a Lagrange Multiplier approach for each portfolio. The results depicted that the maximal returns of Portfolios A, B and C were 4.00%, 4.13% and 7.93% respectively, outperforming the norm portfolio’s average return of 3.69% over the time period 2009-2016.