Tämän pro gradu -tutkielman tarkoituksena on tutkia, onko Euroopan osakemarkkinoilla havaittavissa momentum-anomalia. Tutkielman aikaperiodina on 2002–2016 ja aineistona on STOXX Europe 600 -indeksin osakkeet. Momentum-anomalian olemassaolo on havaittu useissa akateemisissa tutkimuksissa ja on ollut osakemarkkinoilla yli 200 vuoden ajan. Osakemarkkinoiden lisäksi myös valuutta-, velkakirja- ja hyödykemarkkinoilla on havaittavissa momentum-anomalia. Momentum rikkoo tehokkaiden markkinoiden hypoteesin heikointa ehtoa ja sen olemassaololle ei ole yksimielistä selitystä. Momentumia testataan käyttäen 3, 6, 9 ja 12 kuukauden muodostamis- ja pitoaikoja. Euroopan osakemarkkinoilla on havaittavissa selvä momentum-anomalia. Voittajaportfolio tuottaa häviäjäportfoliota paremmin kaikilla muodostamis- ja pitoaikojen yhdistelmillä. 6 kuukauden muodostamis- ja pitoajalla voittajaportfolio tuottaa keskimäärin 21,51 % vuodessa. Häviäjä- ja nollakustannusportfolioilla tuotto on 15,89 % ja 5,22 %. STOXX Europe 600 -indeksi tuottaa vastaavana aikana keskimäärin 13,35 % vuodessa. Momentumia testataan myös CAPM- ja Fama-French-Kolmifaktorimalleilla. Nämä riskimallit eivät kykene selittämään momentum-tuottoja. Finanssikriisi sijoittuu tutkielman aikaperiodiin. Finanssikriisin jälkeen nollakustannusportfolion havaitaan tuottavan valtavat tappiot. Tämä johtuu häviäjäportfolion osakkeiden suuresta arvonnoususta. Tutkielmassa testataan myös, paraneeko momentum-tuotot, kun normaalin momentum-strategian sijasta käytetään dynaamista momentum-strategiaa. Dynaaminen momentum-strategia tuottaa normaalia momentum-strategiaa paremmin. Voittaja- häviäjä- ja nollakustannusportfolioilla tuotot ovat 24,81 %, 11,82 % ja 12,30 % vuodessa.

Many literatures conducted on mutual fund define its popularity among investors and financial analyst. The literatures can be basically summarized in three groups; Test of fund manager’s skills and stock selection, Analysis of fund characteristic and its performance, and lastly on the persistence of fund performance. From the balanced mutual fund data domiciled in Finland, this thesis study the role of active allocation fund manager of Finland and find if they can add extra value to their investors. With the unique sample data and benchmark market employing regression and non-regression methodology; this thesis analyzes the emerging market of developed country. This thesis examines 15 balanced mutual funds of Finland through various existing models such as single and multifactor models, market timing model, Sharpe ratio and information ratio to provide empirical findings on performance and active role of Finnish fund managers. The findings were in line with various prior literatures on mutual fund showing that balanced fund managers do not add extra value to the fund. Most of the risk adjusted abnormal returns are equal to zero or negative. Further, more than half of the sample fund managers possess insufficient skills in adding extra return for risk taken. The excellent Rsquare value we got in every applied models, suggest a good working of model and have impact in explanatory power in the analysis. Lastly, Finnish balanced mutual fund investors get better return by changing their strategy of investment to available options within the company.

Brexit Referendum and US Presidential Election were the two most influential world events in the year 2016. Because of their unexpected results, stock markets around the world experienced lots of turbulences in their stock prices. Brexit Referendum mostly affected the European markets as the future of UK and rest of the Europe are dependent on the result of the event. US Presidential Election, on the other hand, was most likely to affect the US market. But USA has very close business and economic ties with European Union. So, the result of the event is also expected to affect the stock markets of Europe. This thesis has actually conducted an event study on these two events affecting the European markets. For the research, this study has divided the European market into two sub regions- UK and Europe excluding UK. This study found that the event of Brexit Referendum significantly affected the stocks returns on the second day of the event. But US Presidential Election 2016 did not significantly affect the stocks returns of both the regions. Markets seemed to recover and bounced back again within few days of the shocks. So, this could suggest an opportunity for stock investors to accumulate large capital gains.

Investor’s dilemma is: “How to earn the highest possible return with the lowest possible risk.” Yet, if we understood better what is driving the returns and risks, our portfolios could become better performing and diversified. We would also potentially encounter less unpleasant surprises during economic downturns. This seemingly easy question has become a challenging one since investors have failed to diversify portfolios well enough especially during bad times. Factors are currently a popular topic in the financial industry. Yet, majority of the literature focuses on the significance of certain factors and less on how to apply factors in practice. Risk parity optimization serves an attractive alternative for optimizing portfolio weights. The aim of this study is to analyze whether our attention should be directed from asset classes to factors and what benefits such drift could possibly entail. Therefore, the research questions are organized as follows: 1. How chosen factors perform during different periods? 2. What factors seems to be the most persistent out of the six chosen factors? 3. How optimized portfolios perform with respect to the control portfolios? 4. How the chosen portfolio optimization methods work in terms of returns and risks? 5. What is the risk distribution of each constructed portfolio? 6. What do factors reveal from the MSCI World index? We focus on the practical part of portfolio management and aim at outperforming a global equity fund. We use index performance data from the Morgan Stanley Capital International (MSCI) and our broad sample period is 30th Nov 1998 – 31st May 2018. The study is conducted by using long only factor exposures and static weights. As we do not need constant rebalancing and also because currently there are existing low cost ETFs to all used indices, trading costs are not included. This study shows how an attractive risk return based portfolio is constructed using Value, Momentum, Size, Quality, Low Volatility and Dividend factors. We optimize six static portfolios using different risk parity optimized methods and compare their performance to two benchmark portfolios; Equal Weighted (EW) or often called 1/N along with Restricted Minimum Variance. First contribution of this study is that by combing the factors together investors ensure that they are exposing portfolio to the best performing factors. In addition to that, this study verifies that risk optimized portfolios lead win the horse race with EW. Yet, Restricted Minimum Variance portfolio is able to achieve the most attractive risk return tradeoff and wins the competition, but Beta Risk Parity offers better solution from the diversification point of view. Unlike than MVR that chooses only two factors; BRP uses all the six of them. Furthermore, this study confirms that it is generally advisable to shift attention from an asset class-based allocation towards the risk-based allocation. This is due to the fact that the performance of each sample portfolio seems to beat the performance of MSCI World. Such portfolio offers a more favorable return and risk reward relation that should be the simple goal of each rational investor.

This thesis investigates whether the foreign exchange markets are efficient in the emerging economies. The emerging economies include Brazil, China, India, Russia, and South Africa. The motivation behind this research is to provide an insight to the investor whether it is a worthwhile decision to invest in the foreign exchange markets. In order to prove the efficiency of the foreign exchange markets, three hypotheses were tested — the uncovered interest rate parity (UIP) condition, the unbiased forward rate hypothesis (UFH)/rational expectations hypothesis (REH) and the efficient market hypothesis (EMH). The examination is performed using various econometric tools. These include the OLS, VAR, VECM and the Johansen cointegration test. The time-series data used for this research was obtained from the Thompson Reuters Datastream, the website of Federal Reserve Bank of St. Louis (FRED) and the website of the Organization for Economic Co-operation and Development (OECD). The time period for this research was from 31st March 2004 to 30th April 2018. We find that the UIP condition does not hold for any of the BRICS countries. The forward rates are not the unbiased predictors of the spot rate for any of the BRICS countries. The spot and forward rates were cointegrated for the Brazilian real, the Chinese yuan and the Russian ruble. Whereas, the spot and forward rates were not cointegrated for the Indian rupee and the South African rand. The rate of depreciation and the forward rate premium for India and South Africa are cointegrated but the same for the Chinese yuan are not cointegrated.

This paper gives an overview of the research that has been conducted regarding neural networks in option pricing. The paper also analyzes whether a deep neural network model has advantages over the Black-Scholes option pricing model in terms of pricing and hedging European-style call options on the S&P 500 stock index with data ranging from 1996 to 2014. While the previous literature has focused on shallow MLP-styled neural networks, this paper applies a deeper network structure of convolutional neural networks to the problem of pricing and hedging options. Convolutional neural networks are previously known for their success in image classification. The first chapters of this paper focus on both introducing neural networks for a reader, who is not familiar with the topic a priori, as well as giving an overview of the relevant previous literature regarding neural networks in option pricing. The latter chapters present the empirical methodology and the empirical results. The empirical study of this thesis focuses on comparing an option pricing model learned by a convolutional neural network to the Black-Scholes option pricing model. The comparison of the two pricing models is two-fold: the first part of the comparison focuses on pricing performance. Both models will be tested under a test set of data, computing error measures between the price predictions of each model against the true price of an option contract. The second part of the comparison focuses on hedging performance: both models will be used in a dynamic delta-hedging strategy to hedge an option position using the data that is available in the test set. The models are compared to each other using discounted mean absolute tracking error as a measure-of-fit. Bootstrapped confidence intervals are provided for all relevant performance measures. The empirical results are in line with the previous literature in terms of pricing performance and show that a convolutional neural network is superior to the Black-Scholes option pricing model in all error measures. The pricing results also show that a convolutional neural network is better than neural networks in previous studies with superior performance in pricing accuracy also when the data is partitioned by moneyness and maturity. The empirical results are not in line with the previous literature in terms of hedging results and show that a convolutional neural network is inferior to the Black-Scholes option pricing model in terms of discounted mean absolute tracking error. The main findings show that combining a neural network with a traditional parametric pricing formula gives the best possible outcome in terms of pricing and hedging options.

The season of extremely low interest rates has lasted several years, and this can be one reason that housing investing is very popular nowadays. Combining of the surveys made by Finnish Landlord Association (2017) and Danske Bank (2017) show that the young generation (18–29 years old) owns the biggest part of the private housing investments in Finland and apartments are usually highly leveraged. According to surveys, the majority of investors enjoys capitalization rate from 3% to 7 %, but 23% of investors does not know or does not ever calculated the profitability of their investment. At the same time, 53% of investors does not hedge against the rise of interest rates. High leverage ratio, investments done only during the low interest rates, no hedge and ignorance of the profitability of own investment is dangerous combination. This paper focus on the effects to investor’s housing income when the interest rates are rising in the future for a reason or another. The logical approach to this question is to examine capitalization rates (CAP rate) of leveraged housing investor. CAP rate is a simple formula to measure the profitability of real estates or housing units. The original layout, net operating income divided by market value, does not usually consider the possible leverage. However, housing investing is generally highly leveraged, so we do several assumptions to capture the realistic behaviour of leveraged investors and then modify the formula to respond the assumptions. The results of our study are in line with the tightened loan cap regulations set by Financial Supervisory Authority of Finland in July 2016. 100% leverage ratio is not recommendable, but the negative effects of rising interest rates decline sharply when the leverage ratio declines. Our study provides practical approach to consequences of a possible interest rate rise to Finnish housing investing from the private housing investor’s point of view. The paper works also as a guideline to new private investors to illustrate the interest rate risks better. The provided CAP rate formula of leveraged housing investing helps new investors to figure out the realistic profit of their investments.

The aim of this master’s thesis is to provide further evidence on the performance of emerging hedge funds and on the differences, they may have compared with the older and larger hedge funds. We study the style-adjusted alphas of emerging hedge funds relative to similar hedge funds that employ the same strategies. We also inspect the associated performance persistence, size effects and strategy differences within our emerging hedge fund population. The prior literature on the performance of emerging hedge fund suggests that emerging funds are able to provide significant alpha during their early operational life’s when compared with the more established hedge funds. Existing literature has associated emerging hedge funds with characteristics of being nimbler in their investment strategies and fee structures. In this thesis, we have collected our data from two commercial hedge fund databases. These databases were Lipper TASS and HFR, where we collected all the fund related data from 1996 until 2011 in TASS and from 1996 until 2017 in HFR. Based on these two databases we have formed a combined data sample, where we have all the unique funds from both databases. The main analysis of the thesis is based on style-adjusted alphas and we employ two types of time alignment methods, where the first one is based on the event time and the second one is based on cohorts formed by the calendar years. Our first evidence suggests that findings of prior literature on performance of emerging hedge funds have deteriorated in magnitude. We find that style-adjusted performance of emerging funds is substantially lower than previous literature has suggested. In our cohort analysis, we noticed that emerging hedge funds are subject to over time deteriorating performance and they were only able to provide positive style-adjusted alpha during the first year of their operations. In our data, the mid-sized funds performed the best during the launch instead of the larger funds that usually have been seen to perform the best during the initial launch. Our second finding indicates that the emerging hedge funds have not been able to provide a positive style-adjusted alpha after the financial crisis of 2008. Thirdly, we find evidence that when dividing emerging hedge funds into broad strategy classifications, the directional traders classification was the only strategy classification among emerging hedge funds that were able to deliver positive average alpha during our time series. This finding suggests that the positive style-adjusted performance that we saw for our whole emerging hedge fund return series is driven to a great extent by this sub-sample of emerging hedge funds and do not represent the whole industry of emerging funds. Based on the findings of this thesis, investors who allocate capital towards emerging funds and managers, would be able to achieve higher relative returns and diversification benefits compared with the more established hedge funds, if they focus on investing in emerging hedge funds that belong to directional traders broad strategy classification, as the whole emerging hedge funds industry has not been able to deliver relative alpha. Therefore, allocating capital broadly towards emerging hedge funds is not a valid investment strategy to diversify existing hedge fund portfolio unlike prior literature may have suggested.

<p> Most derivative and equity transactions occur in electronic order driven markets and depend on a limit order book. Yet many questions remain regarding the way traders interact with the limit order book, especially the role of algorithmic and high frequency trading. This dissertation investigates how the limit order book evolves over time. We study the nature of fleeting liquidity and flash quotes to deepen our understanding of the way modern markets operate. </p><p> This research is based on raw message data sold by the exchange and contains every update to the limit order book linked to the top ten levels. We rebuild the limit order book and define quote segments to divide the day into non-overlapping intervals based on observed changes in the best quotes and the bid-ask spread. We propose a novel way to visualize dynamics of the limit order book by combining changes in best quotes and visible depth. Using the limit order book and quote segments, we define a measure for offered liquidity and then a measure to capture the responsiveness on both sides of the market during sub-second intervals. Flash quotes are identified and are combined with measures of offered liquidity to study why such behavior is observed in the market.&nbsp; </p><p> We find empirical evidence that movement in market depth explains movement in the bid-ask spread. We show how combining movements in best quotes and visible depth provides a clearer picture of the direction of the market. Evidence is presented that breaks down the dynamics of offered liquidity into both trade response and prior movement of depth. We find standard measures of market liquidity, such as the bid-ask spread, can appear normal while responsiveness can remain elevated following a major market movement.&nbsp; </p><p> Depth data assists with best execution, but this research highlights alternative uses that are important to consider when participating in modern markets. The observed dynamics of the limit order book contain relevant information that need to be captured in a full discussion of market liquidity. </p><p>

<p> Information intermediaries in financial markets improve firms&rsquo; financing and investment efficiency by reducing information asymmetries between firms and investors. The theoretical literature suggests that competition in information production could lead to more transparent information environment and more efficient resource allocation. In my dissertation, I empirically examine how competition among information intermediaries improves firms&rsquo; cost of financing and innovation. </p><p> In the first chapter, I study how competition among stock analysts affects firms&rsquo; product innovation outcomes by examining how analyst coverage, measured by the number of analysts following a firm, influences the introduction of breakthrough new products. Using brokerage house mergers and closures as quasi-experiments, I find that lower analyst coverage leads to fewer breakthrough new products. This positive effect of analyst coverage is concentrated among firms with lower initial coverage, higher growth opportunities, and among firms that have introduced breakthrough new products prior to the analyst shocks. </p><p> In the second chapter, we investigate how the arising of informed traders&mdash;short sellers, increases competition among information producers and reduces the role of banks as information monopolies. We use a regulatory experiment, Regulation SHO, which relaxes short selling constraints for a group of randomly selected stocks. We find that firms affected by SHO enjoyed a 21 basis point lower loan spread, while non-price loan terms of these treated firms were not affected. We also find that while firms without bank loan contracts exhibited a significant decline in stock prices upon the announcement of SHO, firms with bank loan contracts did not react. </p><p> In the third chapter, I study the impact of bank competition on the re-allocation of bank credits and innovation among industries. Using the Interstate Bank Deregulation as a shock to bank competition, I find that the average effect of bank deregulation on corporate innovation is negative. However, firms in R&D; intensive industries experienced significant increase in the number of breakthrough new products, patents and patent citations. </p><p>