Med hjälp av årlig prisstatistik avseende försäljningar av skogsfastigheter (1995-2020) bedömer studien skogliga investeringars marknadsrisk samt estimerar dess diskonteringsränta. Analysen sker inom de teoretiska ramverken Capital Asset Pricing Theory (CAPM) samt Arbitrage Pricing Theory (APT). Utöver korrelation med marknaden analyseras ett antal riskfaktorer så som inflation, förändringar i bostadspriser, BNP samt förändringar i virkespriser. CAPM beräknas för olika löptider där den riskfria räntan matchas mot investeringens tidshorisont. Resultatet ligger i linje med tidigare forskning och visar att skogliga investeringar har en låg marknadsrisk och troligen kan ge ett skydd mot inflation. Val av korrekt löptid för den riskfria räntan har betydande effekt på den estimerade diskonteringsräntan.

We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictabil- ity of returns using neural networks models decreases with longer forecasting horizon. We also document the profitability of long-short portfolios, which were created using predictions of cumulative returns at various horizons, be- fore and after accounting for transaction costs. There is a trade-off between higher transaction costs connected to frequent rebalancing and greater returns on shorter horizons. However, we show that increasing the forecasting hori- zon while matching the rebalancing period increases risk-adjusted returns after transaction cost for the U.S. We combine predictions of expected returns at multiple horizons using double-sorting and buy/hold spread, a turnover reduc- ing strategy. Using double sorts significantly increases profitability on the U.S. sample. Buy/hold spread portfolios have better risk-adjusted profitability in the U.S. JEL Classification G11, G12, G15, C55 Keywords Machine learning, asset pricing, horizon pre- dictability, anomalies Title Multi-horizon equity returns predictability via machine learning

The stock market have received a fair amount of attention in the media recently as a result of the ongoing covid-19 pandemic. The question arouse if there is one month in the year that outperforms all other months in the stock market. A well known anomaly in the world of finance referred to as, the January effect, came up to discussion. Earlier studies of this subject have achieved different results and conclusions. Therefore, this study aims to examine if the January effect exists on mid cap and large cap companies on the Swedish stock market. To achieve this, one large cap portfolio and one mid cap portfolio both equally weighted with ten companies each were created. These two portfolios were analyzed with, among others, a well known regression model for season anomalies. The results of this study concludes that the January effect does not exist in neither of the portfolios.

Anomalier ligger till grund för investeringsstrategier som används för att förvalta biljontals dollar. Anomalier frångår vedertagen teori som implicerar att abnormal avkastning inte är möjlig över tid. En anomali som bevisats ge långsiktig abnormal avkastning är lågriskanomalin. Det råder brist på studier som undersökt om lågriskanomalin existerar på den svenska aktiemarknaden. En studie inom ämnet är av praktisk relevans då resultaten kan ligga till grund för investeringsstrategier som genererar abnormal avkastning. Denna studies syfte är att undersöka om en lågriskanomali existerar på den svenska aktiemarknaden under tidsperioden 2008/01/07 - 2019/12/27. För studien används totalavkastningsdata för bolag noterade på Stockholmsbörsen. Studien skapar hedgeportföljer och undersöker genom regression av prissättningsmodellen CAPM om lågriskportföljer ger högre signifikant abnormal avkastning än högriskportföljer. Studien finner inga statistiskt säkerställda resultat för en lågriskanomali. Det antyder att lågriskanomalin inte existerar på den svenska aktiemarknaden och därför saknar praktisk relevans som investeringsstrategi på nämnda marknad.

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.

Objectives: This work investigates the role for the content of print news media in determining asset prices and capital flows in a small open economy (South Africa). Specifically, it examines how much of the daily variation in stock prices, bond prices, trading volume and capital flows can be explained by phrases in the print news media. Furthermore, this work links such evidence to the existing theoretical and empirical literature. Methods: This work employs natural language processing techniques for counting words and phrases within articles published in national newspapers. Variance decompositions of the resulting word and phrase counts summarise the information extracted from national newspapers in this way. Following previous studies of the United States, least squares regression relates stock returns to single positive or negative 'sentiment' factors. New in this study, support vector regression relates South African stock returns, bond returns and capital flows to the high-dimensional word and phrase counts from national newspapers. Results: I find that domestic asset prices and capital flows between residents and non-residents reflect the content of domestic print news media. In particular, I find that the contents of national newspapers can predict 9 percent of the variation in daily stock returns one day ahead and 7 percent of the variation in the daily excess return of long-term bonds over short-term bonds three days ahead. This predictability in stocks and bonds coincides with predictability of the content of domestic print news media for net equity and debt portfolio capital inflows, suggesting that the domestic print news media affects foreign residents' demand for domestic assets. Moreover, predictability of domestic print news media for near future stock returns is driven by emotive language, suggesting a role for 'sentiment', while such predictability for stock returns further ahead and the premium on long-term bonds is driven by non-emotive language, suggesting a role for other media factors in determining asset prices. These results do not seem to reflect a purely historical phenomenon, finite-sample biases, reverse causality, serial correlation, volatility or day-of-the-week effects. The results support models where foreign agents' short-run beliefs or preferences respond to the content of domestic print news media heterogeneously from those of domestic agents, while becoming more homogeneous in the medium term.

Cette thèse de doctorat comprend trois articles indépendants sur la finance des ménages et l’évaluation des actifs. Les deux premiers articles sont étroitement liés, utilisent des données similaires et étudient le rôle du risque de revenu du travail dans le choix du portefeuille. Le troisième article étudie un modèle de volatilité basé sur le model de Markov-switching multifractal. Le premier article est intitulé "Countercyclical Income Risk and Portfolio Choices" (avec Sylvain Catherine et Paolo Sodini). En utilisant les données du panel administratif suédois sur les salaires et les choix de portefeuille des particuliers, nous montrons que le risque de du revenu contracyclique réduit la volonté des ménages d'investir sur le marché financier. Le deuxième article est intitulé "Seeking Skewness". À l'aide de données administratives détaillées des ménages suédois sur les portefeuille et le revenu du travail, cet article examine le comportement des investisseurs de la demande d'asymétrie dans leur choix de portefeuille. Le troisième article est "Multifractal Volatility with Shot-noise Component" (avec Laurent Calvet). Baser sur le modèle Markov Switching Multifractal (MSM) de Calvet et Fisher (2004), nous développons dans cet article un modèle de volatilité multifractale à temps discret pour capturer des sauts et des décroissances dans le processus de volatilité. / This doctoral thesis consists of three independent papers in household finance and empirical asset pricing. The first two papers are closedly related, use similar data, and investigate the role of labor income risk in portfolio choice. The third paper studies volatility model based on Markov switching multifractal. The first paper is “Countercyclical Income Risk and Portfolio Choices” (with Sylvain Catherine and Paolo Sodini). Using Swedish administrative panel data on individual's wages and portfolio holdings, we show that countercyclical labor income downside risk reduces households' willingness to invest in financial market. The second paper is “Seeking Skewness”. Using detailed disaggregated Swedish household administrative data on portfolio holdings and labor income, this paper investigates retail investors’ behavior of seeking skewness in their portfolio choice. The third paper is “Multifractal Volatility with Shot-noise Component” (with Laurent Calvet). Based on the Markov Switching Multifractal (MSM) model of Calvet and Fisher (2004), we develop in this paper a discrete-time multifractal volatility model to capture the jump and decay pattern in the volatility process along with other stylized facts.

This dissertation is comprised of two papers. The first paper (Chapter 1) provides the theoretical foundation for the estimation of systematic downside risk. Using a new approach, I derive a measure of downside systematic risk, downside beta, that is free of the endogeneity problem and thus straightforward to calculate. Since there is no consensus in the literature regarding the appropriate method for the estimation of downside beta, I review the alternative specifications proposed in the past. I explicitly show that the derived formula here is more efficient in capturing downside risk on both theoretical grounds and in terms of empirical results. Using this efficient specification of systematic downside risk, I show that downside beta has increased explanatory power towards the cross-section of equity returns as compared to unconditional beta. In particular, downside beta predicts larger and more significant future premia, insignificant intercepts in portfolio cross-section tests and cannot be subsumed by additional risk factors proposed in the past literature. I attribute this superior performance to the ability of downside risk to capture distress risk and to the fact that it does not penalize (reward) good (bad) events in good states. In the second paper (Chapter 2) that is co-authored with my advisor, Gregory Kadlec, we exploit the notion of downside risk to explain a long-withstanding market anomaly; the long-term stock return reversals. We show that downside betas of past losers are significantly greater than downside betas of past winners, and the inclusion of downside beta in Fama-Macbeth regressions subsumes the reversal effect. / Ph. D.

The objective of this dissertation is to study the dynamics of size and value risk premia in an equilibrium model with belief dependent preferences and to analyze the impact of investor attention on asset pricing. There is ample evidence that size and value risk premia are non-constant and vary over the business cycle. Empirical patterns, however, are unknown and traditional equilibrium models cannot fit the observed dynamic patterns. The representative agent model with belief dependent preferences is known to fit both unconditional moments such as the equity premium as well as times-series features of volatilities and market prices of risk. The basic model is extended to capture the dynamics of size and value risk premia. The representative agent in this model is a rational Bayesian decision maker who updates her beliefs continuously when new information arrives. However, information processing costs are non-zero and opportunity costs of non-continuous updating of beliefs are higher during times of crisis. In the second part of this dissertation, the representative agent model with beliefs dependent preferences is extended to incorporate the notion of investor attention. The attention version of the model is shown to increase the dynamic fit of equilibrium asset pricing quantities by dampening the volatility of bond yields, market prices of risk, and stock volatility. As such the inattention version of the model with belief dependent preferences is shown to improve the intertemporal fit. Chapter 1 provides a overview of existing studies about the dynamics of size and value risk premia and investor attention. Chapter 2 investigates the dynamic features for size and value risk premia. An asset pricing model with regime dependent risk aversion and incomplete information about economic regimes is introduced to derive closed-form formulas for market prices of risk, asset prices, their volatilities, and risk premia of value and size style indices. Both size and value risk premia vary across normal, recession and boom periods. The premia amplify in recession times but tend to reverse or disappear during boom times. Such findings match the historical performances of small-minus-big (SMB) and high-minus-low (HML) portfolios. Chapter 3 integrates investor attention into regime-switching learning model with regime-dependent risk aversion. The model provides a good fit to the time series of stock volatility, bond volatility and bond yields. Investor attention at the aggregate level is captured by a new representative agent measure which combines the continuously updated beliefs about regimes of a rational Bayesian decision maker with those of a decision maker using steady state regime probabilities. The new representative agent measure can capture the scenario where investor updates her beliefs about economic regimes according to time-varying attention to the available market information. Equilibrium asset pricing quantities are obtained in closed form in the extended model with investor attention. Unconditional asset pricing model moments match their empirical counterparts including the equity premium, the stock volatility and the correlations between stock returns and consumption and dividends. Dynamics features of the data can be well captured. Stock and bond volatilities, bond yield and interest rate time series all have smaller mean square errors compared to the model which does not consider investor attentions. The scale and volatilities for these financial time series are also close to real financial data.

This thesis study two promising methods used to define the multiscale CAPM - the wavelet-based decomposition and the fractal regression. Their estimates, obtained on monthly excess return on ten portfolios formed on beta in the US market, are compared in the period from November 2000 to October 2020 and, subsequently, in the period from November 1965 to October 2020. In the first period, the multiscale beta is not significantly different from the original single-scale beta for most of the portfolios. Contrary, both methods uncover significant multiscale behavior of the beta in the second period. Specifically, the high-beta portfolios have higher multiscale beta at longer investment horizons, mainly at wavelet scale 3 and scales 12-24 of the fractal regression. Overall, both methods deliver consistent results, and seem suitable for extending the CAPM with an investment horizon. JEL Classification Keywords G12, C20 CAPM, asset pricing, multiscale analysis, wavelets, fractal regression Title Investment horizon in the CAPM: A comparison of a wavelet-based decomposition and the fractal regression