La présente étude a pour objectif d'étudier l'évolution du prix des actions au cours de la période entourant l'annonce du bénéfice annuel des entreprises ayant fait appel à l'épargne publique dans le cadre du Régime d'Épargne-Actions du Québec (REAQ). Nous avons fait le même exercice avec un autre groupe d'actions non-subventionnées à l'achat pour fins de comparaison. Une seule grande question est posée: l'annonce des résultats annuels dans les publications financières spécialisées influence-t-elle le cours des actions des entreprises concernées ? Deux techniques sont utilisées pour observer l'évolution des rendements autour de la date d'annonce: la technique des résidus moyens cumulés et celle du portefeuille de référence. Les résultats obtenus tendent à démontrer que la bourse de Montréal serait un marché efficient autant pour les titres du RÉAQ que pour ceux de l'échantillon de contrôle sur la période 1985-1991. Au cours de la période d'annonce et pour les deux groupes de titres, aucun rendement anormal n'est statistiquement différent de zéro. De plus, le marché semble réagir de façon plus significative face à une baisse des bénéfices qu'il ne le fait pour une hausse.

Mutual funds have developed tremendously since 1990s that has drawn attention and required deep study. Their explosive growth are encouraged from the globalization and accelerated from the integration of many financial markets around the world. Following the popular trend, the thesis focuses on assessing the performance of mutual funds. The uniqueness of this study is that we have merged the characteristics of developed market and emerging market by adopting the Asian equity mutual funds marketed in Finland into the sample data. With different measures such as Carhart four-factor, market timing, conditional models and information ratio analysis, 20 mutual funds’ performances were analyzed and evaluated over the period of January 2000–December 2013. Using all the methodologies mentioned, the mutual funds chosen underperformed the benchmark selected and the managers are incapable of timing the market, which proves the previous studies’ results of many researchers. Our findings from conditional models confirms the conclusion of Ferson and Schadt (1996) that the conditional version did better in explaining the cross-sectional variation in returns and risks. The adjusted R squared of the models increased remarkably and alphas became more positive; however, the empirical result still delivered that the Asian funds marketed in Finland did not create value for investors.

More than fifty variables have been shown to have predictive power over expected returns. In a sample of NYSE/AMEX stocks, Lee and Swaminathan (2000) show that share turnover is negatively related to future returns. Also Wang and Chin (2004) find that portfolios of low trading volume outperform portfolios of high trading volume with the difference in returns more pronounced for past winners than for past losers in a sample of Shanghai and Shenzhen stocks. Motivated by these findings, I extend the study on the volume–return dynamics using data from the Helsinki Stock Exchange. In this paper, I study the informational content present in trading volume in the prediction of expected returns. More importantly, I examine the profitability of various volume based portfolios. Following the methodology of Jegadeesh and Titman (1993) and inspired by Wang and Chin (2004), I form portfolios based on past returns and trading volume over different formation and holding periods and examine the profitability of various volume based portfolio strategies over the period from 1999 to 2010. I find that conditional on past returns, high-volume stocks outperform low volume stocks for most formation and holding periods. The volume discount, that is the average monthly returns difference between low volume portfolios and high-volume portfolios, is largest in magnitude for the 6 x 12 strategy for winners portfolios at -2.17% (t-statistics = −4.70). The results are robust to risk adjustment relative to the Fama and French (1993) three factor model. The results are not entirely consistent with the existing literature on the volume-return dynamics. While the results suggest that investing in high-volume stocks and shorting portfolios of low-volume stocks is a profitable strategy, implementing such trading strategy should be done with caution because of the special nature of the Helsinki Stock Exchange.

In this study, we derive a new measure of aggregate systemic risk, denoted ASR, from the Hedge Fund Research (HFR) database that complements systemic risk of hedge funds by forecasting future asset returns up to six months ahead. We examine the ability of ASR measure of hedge funds to predict fixed income and equity returns. We apply the methodology following Linda, Bali, and Tang (2012) to estimate ASR and construct the main framework of this study. Subsequently, the micro level systemic risk exposure of individual hedge funds, that is 5% value at risk (VaR) of each fund, is aggregated. The VaR of each fund is estimated using a non-parametric quantile approach. This method is appropriate because usually hedge funds have shorter return series. ASR is then constructed as cross sectional average of VaR estimates of funds over a fixed rolling window of thirty six months. We run multiple time series regression models to predict asset returns. The regression results reveal mix evidence of predictability because ASR measure of hedge funds is able predict some asset returns while others remain unpredictable. Most of the U.S. constant maturity yields, especially five years and higher, are predictable along with regional ETFs. On the contrary, other assets show no strong evidence of predictable because only few countries’ bonds and or ETFs are predictable. Results indicate predictability improves slightly after adjusting hedge fund data for backfill bias. Based on the broad strategies of hedge funds, results suggest that systemic risk among hedge funds under multi-process and security selection have predictability stronger than other categories. Smaller funds seem to have no impact on predictability implying that predictability is mostly driven by larger funds. Predictability results across different time periods suggest that the ability of ASR to predict asset returns is strongest during 2003–2007 period followed by 1997–2002 period. In contrast to these findings, predictability drops down at longer horizons. The long term predictability i.e. two to six months ahead, tends to drop because the number of assets predictable by ASR decline for all assets except for constant maturity yields. These results indicate that micro level risk exposure of hedge funds when aggregated provides a tool to forecast asset returns. Although, not all assets are predictable by ASR, it has the potential to be used as a forecasting tool in conjunction with other forecasting tools to enhance the forecast accuracy.

Hedge-rahastot voivat osakkeenomistaja-aktivisteina toimimalla saavuttaa huomattavia epänormaaleja tuottoja. Niin kutsutun aktivististrategian on toistuvasti havaittu päihittävän sekä markkinaindeksi että monet muut hedge-rahastojen käyttämät sijoitusstrategiat. Aktivismin teho perustuu siihen, että kohdeyrityksen osakkeen hinta reagoi positiivisesti aktivistirahaston hankkiessa siitä omistusosuuden ja toteuttaessa aktivistisia toimia. Tässä tutkielmassa havaitaan, että aktivististrategian ylivoimaisuus on kuitenkin riippuvainen tarkasteluperiodista. 1990-luvun lopussa ja 2000-luvun alussa aktivistirahastot saavuttavat selvästi muita rahastoja suurempia tuottoja, mutta vuoden 2005 jälkeen eroja ei juuri ole nähtävissä. Vuoden 2008 finanssikriisin aikana aktivistirahastot kärsivät muita hedge-rahastoja suurempia tappioita. Osakkeenomistaja-aktivismilla tarkoitetaan oman omistusosuuden kautta tapahtuvaa vaikuttamista osakeyhtiön hallinnossa. Hedge-rahastojen tavoitteena on erilaisia keinoja käyttäen nostaa kohdeyrityksen osakkeen hintaa ja näin hyötyä aktivismista rahallisesti. Hedge-rahastot ovat löyhästi säänneltyjä ja monimutkaisia strategioita käyttäviä yksityisiä rahastoja, joiden managereilla on suuret kannustimet tavoitella mahdollisimman runsaita voittoja. Erityispiirteidensä takia hedge-rahastot menestyvät osakkeenomistaja-aktivismissa yksityishenkilöitä ja perinteisiä institutionaalisia sijoittajia paremmin. Tutkielmassa huomataan, että aktivistirahastot eroavat ominaisuuksiltaan huomattavasti muista hedge-rahastoista, mutta rahaston kokoa lukuun ottamatta näiden piirteiden avulla ei pystytä tunnistamaan erityisen menestyviä aktivisteja. Tutkielmassa käytetty kansainvälinen aineisto on poikkeuksellisen laaja, ja eri vääristymiä kontrolloidaan tehokkaasti. Tutkimusaineisto sisältää aikavälillä 1995–2012 lähes 30 000 yksittäistä rahastoa ja yli 1 500 000 aikasarjahavaintoa rahastojen tuotoista ja hallinnassa olevista varoista. Aktivistirahastoja aineistosta tunnistetaan yhteensä 344 kappaletta. Tuottojen analysoinnissa pääosassa ovat erilaiset aikasarjaregressioihin perustuvat multifaktorimallit. Tarkastelun kohteena ovat mallien avulla estimoidut alfat sekä altistuminen eri faktoreille. Tutkielman tulokset ovat yleistettävissä aineiston laajuuden ja useiden saman tuloksen antavien analyysien johdosta. Hedge-rahastojen osakkeenomistaja-aktivismi on viime aikoina saanut paljon julkisuutta kohdeyritysten ollessa vuosi vuodelta yhä suurempia. Aihe on yhteiskunnallisesti merkittävä, koska aktivismi vaikuttaa paitsi hedge-rahastojen sijoittajiin, myös kohdeyrityksiin. Aktivismin puolestapuhujat korostavat aktivismin kehittävän yritysten tuottavuutta, ratkaisevan päämies–agentti -suhteen ongelmia ja parantavan osakkeenomistajien asemaa. Aktivismin kritisoijat pelkäävät hedge-rahastojen tavoittelevan lyhyen aikavälin voittoja pitkän aikavälin menestyksen kustannuksella. Hedge-rahastojen osakkeenomistaja-aktivismin havaitaan joka tapauksessa olevan varsin menestyksekäs sijoitusstrategia, vaikka sen tehokkuus on viime vuosina hiipunut.

High frequency trading is not only about the speed but also about the effective trading strategies it uses to perform the trade. Performance capacity evaluation of high frequency trading strategies is done using different comparative ratios. Studies find, due to tight spread, it is difficult for high frequency traders to generate significant alpha by trading the highly liquid stocks using market making strategy. But they can still generate positive return with Sharpe ratio almost equal to market. They act more like market makers following this strategy. The capacity of other high frequency trading strategies lies in between (58–75) %. Statistical arbitrage strategy is the best among all the high frequency trading strategies. Sharpe ratio as a main tool of comparison between high frequency and non-high frequency traders, shows multiple times higher Sharpe for high frequency traders in comparison to non-high frequency traders. Value at Risk (VaR) suggests the probability of generating positive return for all the strategies having long and short positions. This thesis takes one month high frequency limit order and tick data from NASDAQ OMX Nordic and select six mostly traded Finnish stocks based on their limit order book activities. Basic limit order book activities of all the selected stocks is analyzed including and excluding non-high frequency activates to make sure all the selected stocks are influenced by high frequency activities, so that the result is more accurate. This thesis follows the high frequency trading strategies and respective holding periods suggested by Aldridge (2009). Sometimes strategies work not because strategies are efficient but due to market inefficiency. This thesis crosschecks the market inefficiency with auto-regressive based test. Due to tick data and a very short time interval between the two observations, it finds strong influence of past returns and past price movements in the current return suggesting inefficiency in the market.

The goal of this master’s thesis is to understand the performance implications of hedge fund’s tail risk, and the mechanisms of how some funds achieve lower tail risk. The current evidence on the performance implications is mixed, with most empirical hedge fund studies suggesting higher returns to higher risk. This is not obvious since the goal of skillful hedge fund managers is to deliver positive risk-adjusted returns, and indeed a few studies do report higher returns to lower risk. The issue is further complicated by the evidence of asset-level low-risk anomalies, which could create a low-skill alternative for managers to achieving higher returns with lower risk. Using a consolidation of commercial hedge fund databases, we decompose hedge fund tail risk, conditional on market distress, into two components: Systematic Conditional Tail Risk (SCTR) arising predictably via equity market exposure, and Idiosyncratic Conditional Tail Risk (ICTR) arising from unpredictable, proprietary alpha investment technology. First, using a subset of large, 13F-HR matched hedge funds from March 2000 to June 2013, we show that especially low-ICTR hedge funds deliver superior future risk-adjusted returns. In contrast to existing hedge fund literature our results support the broader view in asset-pricing literature that low risk is associated with higher risk-adjusted returns. The results are robust to the inclusion of additional risk factors, including a low-risk factor, suggesting that the better performance could be due to skillful hedging rather than harvesting of low-risk anomalies. This skill hypothesis is further supported by the finding that low-risk funds charge higher incentive fees, consistent with economic theory. To further resolve the puzzle of whether low-risk funds outperform high-risk funds, using a large set of funds from January 1994 to June 2013, we run a comprehensive “horse race” between our risk measures and a replication of a large array of existing risk measures. Our results show that for many existing risk measures, the purported risk premium largely diminishes when controlling fund size, suggesting that existing results may be somewhat driven by the inclusion of smaller funds. Our measures SCTR and ICTR consistently show low-risk funds outperforming high-risk funds. Second, using 13F-HR option holdings data from March 1999 to June 2013, we investigate the underlying hedging mechanism implemented by low tail risk hedge funds. We demonstrate that low-SCTR funds allocate a high fraction of their wealth — consistently over time — to protective option strategies, while low-ICTR funds use costly protective strategies only during the financial crisis. Funds with low ICTR also employ more stock, but not index, options, which fits the idiosyncratic nature of the measure. After the financial crisis, volatility-linked Exchange Traded Products (ETPs) have emerged as a potential alternative to hedging tail risk. We show that, from April 2009 to June 2013, the use of such volatility-linked ETPs is associated with lower SCTR but not ICTR, consistent with the option result, and indeed suggesting a complementary hedging mechanism.

This thesis paper test for stock return predictability in the largest and most comprehensive industry data set analyzed so far, using three common forecasting variables: the earnings-price (EP), dividend-price (DP) and book-price (BP) ratios and a new (proposed) predictor variable — the residual equity-price (REP) ratio. Considering the three common forecasting variables, each has price as the denominator, which explicitly represent the value an investor pays to own a share of stock. However, the numerators of these ratios do not reflect the explicit gain or loss to the investor for owning the share of stocks. That is, these three common forecasting variables do not reflect the most vital characteristic of a shareholder being a residual-owner. Thus, evidence of return predictability using these ratios could be interpreted as clueless and as a result of chance. The proposed predictor variable — the REP ratio, provide antidote to this distortion. The data contain over 10,000 quarterly observations from 88 companies listed on the New York stock exchange. The companies (stocks) are pooled into a panel of industries according to the NYSE U.S. 100 Index classifications. I conducted pooled regressions — employing the methodology of Hjalmarsson (2010), as well as time-series regressions for individual companies. The empirical results indicate that the three common financial ratios (EP, DP and BP ratios), as well as the REP ratio are fairly robust predictors of quarterly excess stock returns, in industry dataset. The null of no predictability is clearly rejected in the pooled regression for a number of industry panels, as well as in a number of individual company level time-series regressions. However, considering their out-of-sample performance — the REP ratio tremendously outperforms the historical average forecasts, as well as the forecasts based on the EP, DP and BP ratios. The evidences are generally inline with those found by Lewellen (2004), Campbell and Yogo (2006), and Cochrane (2008), with U.S. data (index data); and Hjalmarsson (2010) with international data.

Tämän työn tarkoituksena on tutkia, miten tiettyihin riskifaktoreihin perustuvat indeksit ovat tuottaneet Yhdysvaltojen markkinoilla aikavälillä 30.11.1998–31.12.2015, sekä verrata eri riskipariteettimenetelmin koostettuja faktorisalkkuja MSCI:n omaan multifaktori-indeksiin. Jo aiemmin akateemisessa ja empiirisissä tutkimuksissa on havaittu tiettyjen riskifaktoreiden selittävän arvopapereiden tuottoja. Yleisesti tunnustettuja ja käytettyjä faktoreita ovat esimerkiksi arvo-, koko-, laatu- ja momentumifaktori. Nämä faktorit ovat tarjonneet sijoittajalle riskipreemion yli markkinatuoton pitkällä aikavälillä. Aiemmin faktoreiden hyödyntäminen sijoitustoiminnassa oli mahdollista ainoastaan perinteisten aktiivisten sijoitusrahastojen kautta, joista monet hyödyntävätkin mekaanisia altistumisia kyseisille faktoreille. Aktiivisten rahastojen rinnalle on kuitenkin 2000-luvulla noussut niin kutsuttuja smart beeta-ETF:iä, jotka tarjoavat altistumisen valitulle faktorille läpinäkyvästi ja alhaisin kustannuksin. Smart beeta-ETF:ksi voidaan kutsua lähes mitä tahansa markkina-arvopainotetusta poikkeavaa strategiaa. Tutkielman tulosten perusteella tarkastellut MSCI:n faktori-indeksit ovat tarjonneet tutkitulla aikavälillä perinteistä markkina-arvopainotettua parempaa tuottoa. Lisäksi eri riskipariteettimenetelmin koostetuilla salkuilla voidaan parantaa portfolion tuotto/riski- suhdetta. Verrattaessa tutkielmassa koostettuja riskipariteettisalkkuja MSCI:n omaan multifaktori-indeksiin, voidaan todeta sen tarjonneet tutkitulla aikavälillä riskipariteettisalkkuja parempia tuottoja. Multifaktorin hyvää menestystä selittää aktiivinen faktoreiden painottaminen markkinatilanteesta riippuen. Sen tuottoja tutkittaessa on hyvä ottaa huomioon, että MSCI:n tarjoama aikasarja indeksille on saatu aikaan aikahistoriasta (Back-tested). Siirtyminen omaisuuslajipohjaisesta sijoittamisesta faktoripohjaiseen, sekä riskipariteettimenetelmät salkun rakennuksessa avaavat sijoittajille laajemmat mahdollisuudet koostaa riski- ja tuotto-ominaisuuksiltaan sopiva salkku.

The dissertation proposes a "speculative leveraging" theory of hostile corporate takeovers in the merger wave of the 1980s. It hypothesizes that takeovers became widespread because intense financial market competition during the 1970s created a short time horizon and a preference for leverage and increasingly high risk assets among shareholders and lenders, while many firms maintained low indebtedness and heavy long term-oriented expenditures. When firms' debt was lower and investment was higher than preferred by stockholders, debt-financed takeovers might create premiums by promising to raise the first and lower the second. Underpinning the speculative leveraging theory is a framework in which financial practices and pricing may be unstable and destabilizing to the real sector of the economy. Because this framework differs sharply from the efficient markets view common in mainstream finance and economics, its theory of the stock market and the firm is discussed at length. Then the institutional and competitive financial market changes of the 1960s and 1970s are examined within that theoretical framework, drawing out the dynamics hypothesized to become important in the 1980s takeover wave. The speculative leveraging theory is specified in a qualitative response raider's choice model. Also so specified is the free cash flow theory of takeovers, the most influential explanation of debt-financed takeovers which lies within the efficient markets tradition. The two models are tested against a comprehensive sample of hostile takeovers over the years 1968-87, along with non-takeover control firms. Statistical results show that the free cash flow model does not explain takeover status, while the speculative leveraging model does so more strongly as hypothesized, during the 1980s. These theoretical, historical, and empirical findings suggest that the premiums accompanying 1980s takeovers should not be assumed to indicate prospective efficiency gains.