Searching for return: How Google search engine query data can beat the market

Throughout the last years technologic improvements have enabled internet users to analyze and retrieve data regarding Internet searches. In several fields of study this data has been used. Some authors have been using search engine query data to forecast economic variables, to detect influenza areas or to demonstrate that it is possible to capture some patterns in stock markets indexes. In this paper one investment strategy is presented using Google Trends’ weekly query data from major global stock market indexes’ constituents. The results suggest that it is indeed possible to achieve higher Info Sharpe ratios, especially for the major European stock market indexes in comparison to those provided by a buy-and-hold strategy for the period considered.

An analysis of herd behavior in Latin American stock markets

This research investigates whether the major stock markets in Latin America (Brazil, Mexico, Chile, Colombia, Peru and Argentina) exhibited herd behavior over the period January 2, 2002 to June 30, 2014, using the variation in the returns overall and by sector in the most representative stock market index in each country, using the model proposed by Christie y Huang (1995) -- The results do not reveal any herd behavior in the total market, or in the sectors of the markets examined in the study

The Survival and Stock Performance of Emerging Country Firms in the United States

Many firms from emerging markets flocked to developed countries at high cost with hopes of acquiring strategic assets that are difficult to obtain in home countries. Adequate research has focused on the motivations and strategies of emerging country firms' (ECFs') internationalization, while limited studies have explored their survival in advanced economies years after their venturing abroad. Due to the imprinting effect of home country institutions that inhibit their development outside their home market, ECFs are inclined to hire executives with international background and affiliate to world-wide organizations for the purpose of linking up with the global market, embracing multiple perspectives for strategic decisions, and absorbing the knowledge of foreign markets. However, the effects of such orientation on survival are under limited exploration. Motivated by the discussion above, I explore ECFs’ survival and stock performance in a developed country (U.S.). Applying population ecology, signaling theory and institutional theory, the dissertation investigates the characteristics of ECFs that survived in the developed country (U.S.), tests the impacts of global orientation on their survival, and examines how global-oriented activities (i.e. joining United Nations Global Compact) affect their stock performance. The dissertation is structured in the form of three empirical essays. The first essay explores and compares different characteristics of ECFs and developed country firms (DCFs) that managed to survive in the U.S. The second essay proposes the concept of global orientation, and tests its influences on ECFs’ survival. Employing signaling theory and institutional theory, the third essay investigates stock market reactions to announcements of United Nation Global Compact (UNGC) participation. The dissertation serves to explore the survival of ECFs in the developed country (U.S.) by comparison with DCFs, enriching traditional theories by testing non-traditional arguments in the context of ECFs’ foreign operation, and better informing practitioners operating ECFs about ways of surviving in developed countries and improving stockholders’ confidence in their future growth.

Is there timing ability in currency markets? Evidence from ADR issuances

Is there timing ability in the exchange rate markets? We address this question by examining foreign firms' decisions to issue American Depositary Receipts (ADRs). Specifically, we test whether foreign firms consider currency market conditions in their ADR issuance decisions and, in doing so, display some ability to time their local exchange rate market. We study ADR issuances in the U.S. stock market between 1976 and 2003. We find that foreign firms tend to issue ADRs after their local currency has been abnormally strong against the U.S. dollar and before their local currency becomes abnormally weak. This evidence is statistically significant even after controlling for local and U.S. past and future stock market performance and predicable exchange rate movements. Currency market timing is especially significant i) for value companies, relatively small (yet absolutely large) companies issuing relatively large amounts of ADRs, companies with higher currency exposure, manufacturing companies, and emerging market companies, ii) during currency crises (when mispricings are rife) and after the integration of the issuer's local financial market with the world capital markets, iii) when the ADR issue raises capital for the issuing firm (Level III ADR), and iv) regardless of the identity of the underwriting investment bank. Currency market timing is also economically significant since it translates into total savings for the issuing firms of about $646 million (or 1.86% of the total capital-raising ADR issue volume). In contrast, we find no evidence of currency timing ability in a control sample made of non-capital raising ADRs (Level II ADRs). These findings suggest that some companies may have, at least occasionally, private information about foreign exchange.

The valuation accuracy of multiples in mergers and acquisitions, and their association with firm misvaluation

This study explores the accuracy and valuation implications of the application of a comprehensive list of equity multiples in the takeover context. Motivating the study is the prevalent use of equity multiples in practice, the observed long-run underperformance of acquirers following takeovers, and the scarcity of multiplesbased research in the merger and acquisition setting. In exploring the application of equity multiples in this context three research questions are addressed: (1) how accurate are equity multiples (RQ1); which equity multiples are more accurate in valuing the firm (RQ2); and which equity multiples are associated with greater misvaluation of the firm (RQ3). Following a comprehensive review of the extant multiples-based literature it is hypothesised that the accuracy of multiples in estimating stock market prices in the takeover context will rank as follows (from best to worst): (1) forecasted earnings multiples, (2) multiples closer to bottom line earnings, (3) multiples based on Net Cash Flow from Operations (NCFO) and trading revenue. The relative inaccuracies in multiples are expected to flow through to equity misvaluation (as measured by the ratio of estimated market capitalisation to residual income value, or P/V). Accordingly, it is hypothesised that greater overvaluation will be exhibited for multiples based on Trading Revenue, NCFO, Book Value (BV) and earnings before interest, tax, depreciation and amortisation (EBITDA) versus multiples based on bottom line earnings; and that multiples based on Intrinsic Value will display the least overvaluation. The hypotheses are tested using a sample of 147 acquirers and 129 targets involved in Australian takeover transactions announced between 1990 and 2005. The results show that first, the majority of computed multiples examined exhibit valuation errors within 30 percent of stock market values. Second, and consistent with expectations, the results provide support for the superiority of multiples based on forecasted earnings in valuing targets and acquirers engaged in takeover transactions. Although a gradual improvement in estimating stock market values is not entirely evident when moving down the Income Statement, historical earnings multiples perform better than multiples based on Trading Revenue or NCFO. Third, while multiples based on forecasted earnings have the highest valuation accuracy they, along with Trading Revenue multiples for targets, produce the most overvalued valuations for acquirers and targets. Consistent with predictions, greater overvaluation is exhibited for multiples based on Trading Revenue for targets, and NCFO and EBITDA for both acquirers and targets. Finally, as expected, multiples based Intrinsic Value (along with BV) are associated with the least overvaluation. Given the widespread usage of valuation multiples in takeover contexts these findings offer a unique insight into their relative effectiveness. Importantly, the findings add to the growing body of valuation accuracy literature, especially within Australia, and should assist market participants to better understand the relative accuracy and misvaluation consequences of various equity multiples used in takeover documentation and assist them in subsequent investment decision making.

Do airlines always suffer from crashes?

We examine the impact of aviation disasters on the stock prices of the crash airlines and their rival airlines. Results show that the crash airlines experience deeper negative abnormal returns as the degree of fatality increases. The stock prices of the rival airlines also suffer in large-scale disasters but benefit from the disasters when the fatality is minor.

Modeling Asymmetries in Financial Data with Multiplicative Error Models

This thesis addresses modeling of financial time series, especially stock market returns and daily price ranges. Modeling data of this kind can be approached with so-called multiplicative error models (MEM). These models nest several well known time series models such as GARCH, ACD and CARR models. They are able to capture many well established features of financial time series including volatility clustering and leptokurtosis. In contrast to these phenomena, different kinds of asymmetries have received relatively little attention in the existing literature. In this thesis asymmetries arise from various sources. They are observed in both conditional and unconditional distributions, for variables with non-negative values and for variables that have values on the real line. In the multivariate context asymmetries can be observed in the marginal distributions as well as in the relationships of the variables modeled. New methods for all these cases are proposed. Chapter 2 considers GARCH models and modeling of returns of two stock market indices. The chapter introduces the so-called generalized hyperbolic (GH) GARCH model to account for asymmetries in both conditional and unconditional distribution. In particular, two special cases of the GARCH-GH model which describe the data most accurately are proposed. They are found to improve the fit of the model when compared to symmetric GARCH models. The advantages of accounting for asymmetries are also observed through Value-at-Risk applications. Both theoretical and empirical contributions are provided in Chapter 3 of the thesis. In this chapter the so-called mixture conditional autoregressive range (MCARR) model is introduced, examined and applied to daily price ranges of the Hang Seng Index. The conditions for the strict and weak stationarity of the model as well as an expression for the autocorrelation function are obtained by writing the MCARR model as a first order autoregressive process with random coefficients. The chapter also introduces inverse gamma (IG) distribution to CARR models. The advantages of CARR-IG and MCARR-IG specifications over conventional CARR models are found in the empirical application both in- and out-of-sample. Chapter 4 discusses the simultaneous modeling of absolute returns and daily price ranges. In this part of the thesis a vector multiplicative error model (VMEM) with asymmetric Gumbel copula is found to provide substantial benefits over the existing VMEM models based on elliptical copulas. The proposed specification is able to capture the highly asymmetric dependence of the modeled variables thereby improving the performance of the model considerably. The economic significance of the results obtained is established when the information content of the volatility forecasts derived is examined.

Problems and Algorithms for Sequence Segmentations

The analysis of sequential data is required in many diverse areas such as telecommunications, stock market analysis, and bioinformatics. A basic problem related to the analysis of sequential data is the sequence segmentation problem. A sequence segmentation is a partition of the sequence into a number of non-overlapping segments that cover all data points, such that each segment is as homogeneous as possible. This problem can be solved optimally using a standard dynamic programming algorithm. In the first part of the thesis, we present a new approximation algorithm for the sequence segmentation problem. This algorithm has smaller running time than the optimal dynamic programming algorithm, while it has bounded approximation ratio. The basic idea is to divide the input sequence into subsequences, solve the problem optimally in each subsequence, and then appropriately combine the solutions to the subproblems into one final solution. In the second part of the thesis, we study alternative segmentation models that are devised to better fit the data. More specifically, we focus on clustered segmentations and segmentations with rearrangements. While in the standard segmentation of a multidimensional sequence all dimensions share the same segment boundaries, in a clustered segmentation the multidimensional sequence is segmented in such a way that dimensions are allowed to form clusters. Each cluster of dimensions is then segmented separately. We formally define the problem of clustered segmentations and we experimentally show that segmenting sequences using this segmentation model, leads to solutions with smaller error for the same model cost. Segmentation with rearrangements is a novel variation to the segmentation problem: in addition to partitioning the sequence we also seek to apply a limited amount of reordering, so that the overall representation error is minimized. We formulate the problem of segmentation with rearrangements and we show that it is an NP-hard problem to solve or even to approximate. We devise effective algorithms for the proposed problem, combining ideas from dynamic programming and outlier detection algorithms in sequences. In the final part of the thesis, we discuss the problem of aggregating results of segmentation algorithms on the same set of data points. In this case, we are interested in producing a partitioning of the data that agrees as much as possible with the input partitions. We show that this problem can be solved optimally in polynomial time using dynamic programming. Furthermore, we show that not all data points are candidates for segment boundaries in the optimal solution.

The economic consequences of financial reporting on Twitter

The use of social media has spread into many different areas including marketing, customer service, and corporate disclosure. However, our understanding of the timely effect of financial reporting information on Twitter is still limited. In this paper, we propose to examine the timely effect of financial reporting information on Twitter in Australian context, as reflect in the stock market trading. We aim to find out whether the level of information asymmetry within the stock market will be reduced, after the introduction of Twitter and the use of Twitter for financial reporting purpose

The economic consequences of corporate financial reporting on Twitter

The use of social media has spread into many different areas including marketing, customer service, and corporate disclosure. However, our understanding of the timely effect of financial reporting information on Twitter is still limited. In this paper, we examine the timely effect of financial reporting information on Twitter in the Australian context, as reflected in the follow-up stock market reaction. With the use of event methodology and comparative setting, we find that financial reporting disclosure on Twitter reduces the information asymmetry level. This is evidenced by reduction of bid-ask spread and increase of share trading volume. The results of this study imply that financial reporting disclosure on social media assists the dissemination of information and the stock market response to this information

Studies on Binary Time Series Models with Applications to Empirical Macroeconomics and Finance

This thesis studies binary time series models and their applications in empirical macroeconomics and finance. In addition to previously suggested models, new dynamic extensions are proposed to the static probit model commonly used in the previous literature. In particular, we are interested in probit models with an autoregressive model structure. In Chapter 2, the main objective is to compare the predictive performance of the static and dynamic probit models in forecasting the U.S. and German business cycle recession periods. Financial variables, such as interest rates and stock market returns, are used as predictive variables. The empirical results suggest that the recession periods are predictable and dynamic probit models, especially models with the autoregressive structure, outperform the static model. Chapter 3 proposes a Lagrange Multiplier (LM) test for the usefulness of the autoregressive structure of the probit model. The finite sample properties of the LM test are considered with simulation experiments. Results indicate that the two alternative LM test statistics have reasonable size and power in large samples. In small samples, a parametric bootstrap method is suggested to obtain approximately correct size. In Chapter 4, the predictive power of dynamic probit models in predicting the direction of stock market returns are examined. The novel idea is to use recession forecast (see Chapter 2) as a predictor of the stock return sign. The evidence suggests that the signs of the U.S. excess stock returns over the risk-free return are predictable both in and out of sample. The new "error correction" probit model yields the best forecasts and it also outperforms other predictive models, such as ARMAX models, in terms of statistical and economic goodness-of-fit measures. Chapter 5 generalizes the analysis of univariate models considered in Chapters 2 4 to the case of a bivariate model. A new bivariate autoregressive probit model is applied to predict the current state of the U.S. business cycle and growth rate cycle periods. Evidence of predictability of both cycle indicators is obtained and the bivariate model is found to outperform the univariate models in terms of predictive power.

Intraday Dynamics of International Equity Markets

The increased availability of high frequency data sets have led to important new insights in understanding of financial markets. The use of high frequency data is interesting and persuasive, since it can reveal new information that cannot be seen in lower data aggregation. This dissertation explores some of the many important issues connected with the use, analysis and application of high frequency data. These include the effects of intraday seasonal, the behaviour of time varying volatility, the information content of various market data, and the issue of inter market linkages utilizing high frequency 5 minute observations from major European and the U.S stock indices, namely DAX30 of Germany, CAC40 of France, SMI of Switzerland, FTSE100 of the UK and SP500 of the U.S. The first essay in the dissertation shows that there are remarkable similarities in the intraday behaviour of conditional volatility across European equity markets. Moreover, the U.S macroeconomic news announcements have significant cross border effect on both, European equity returns and volatilities. The second essay reports substantial intraday return and volatility linkages across European stock indices of the UK and Germany. This relationship appears virtually unchanged by the presence or absence of the U.S stock market. However, the return correlation among the U.K and German markets rises significantly following the U.S stock market opening, which could largely be described as a contemporaneous effect. The third essay sheds light on market microstructure issues in which traders and market makers learn from watching market data, and it is this learning process that leads to price adjustments. This study concludes that trading volume plays an important role in explaining international return and volatility transmissions. The examination concerning asymmetry reveals that the impact of the positive volume changes is larger on foreign stock market volatility than the negative changes. The fourth and the final essay documents number of regularities in the pattern of intraday return volatility, trading volume and bid-ask spreads. This study also reports a contemporaneous and positive relationship between the intraday return volatility, bid ask spread and unexpected trading volume. These results verify the role of trading volume and bid ask quotes as proxies for information arrival in producing contemporaneous and subsequent intraday return volatility. Moreover, asymmetric effect of trading volume on conditional volatility is also confirmed. Overall, this dissertation explores the role of information in explaining the intraday return and volatility dynamics in international stock markets. The process through which the information is incorporated in stock prices is central to all information-based models. The intraday data facilitates the investigation that how information gets incorporated into security prices as a result of the trading behavior of informed and uninformed traders. Thus high frequency data appears critical in enhancing our understanding of intraday behavior of various stock markets’ variables as it has important implications for market participants, regulators and academic researchers.

Modeling and Forecasting Implied Volatility: Implications for Trading, Pricing, and Risk Management

Modeling and forecasting of implied volatility (IV) is important to both practitioners and academics, especially in trading, pricing, hedging, and risk management activities, all of which require an accurate volatility. However, it has become challenging since the 1987 stock market crash, as implied volatilities (IVs) recovered from stock index options present two patterns: volatility smirk(skew) and volatility term-structure, if the two are examined at the same time, presents a rich implied volatility surface (IVS). This implies that the assumptions behind the Black-Scholes (1973) model do not hold empirically, as asset prices are mostly influenced by many underlying risk factors. This thesis, consists of four essays, is modeling and forecasting implied volatility in the presence of options markets’ empirical regularities. The first essay is modeling the dynamics IVS, it extends the Dumas, Fleming and Whaley (DFW) (1998) framework; for instance, using moneyness in the implied forward price and OTM put-call options on the FTSE100 index, a nonlinear optimization is used to estimate different models and thereby produce rich, smooth IVSs. Here, the constant-volatility model fails to explain the variations in the rich IVS. Next, it is found that three factors can explain about 69-88% of the variance in the IVS. Of this, on average, 56% is explained by the level factor, 15% by the term-structure factor, and the additional 7% by the jump-fear factor. The second essay proposes a quantile regression model for modeling contemporaneous asymmetric return-volatility relationship, which is the generalization of Hibbert et al. (2008) model. The results show strong negative asymmetric return-volatility relationship at various quantiles of IV distributions, it is monotonically increasing when moving from the median quantile to the uppermost quantile (i.e., 95%); therefore, OLS underestimates this relationship at upper quantiles. Additionally, the asymmetric relationship is more pronounced with the smirk (skew) adjusted volatility index measure in comparison to the old volatility index measure. Nonetheless, the volatility indices are ranked in terms of asymmetric volatility as follows: VIX, VSTOXX, VDAX, and VXN. The third essay examines the information content of the new-VDAX volatility index to forecast daily Value-at-Risk (VaR) estimates and compares its VaR forecasts with the forecasts of the Filtered Historical Simulation and RiskMetrics. All daily VaR models are then backtested from 1992-2009 using unconditional, independence, conditional coverage, and quadratic-score tests. It is found that the VDAX subsumes almost all information required for the volatility of daily VaR forecasts for a portfolio of the DAX30 index; implied-VaR models outperform all other VaR models. The fourth essay models the risk factors driving the swaption IVs. It is found that three factors can explain 94-97% of the variation in each of the EUR, USD, and GBP swaption IVs. There are significant linkages across factors, and bi-directional causality is at work between the factors implied by EUR and USD swaption IVs. Furthermore, the factors implied by EUR and USD IVs respond to each others’ shocks; however, surprisingly, GBP does not affect them. Second, the string market model calibration results show it can efficiently reproduce (or forecast) the volatility surface for each of the swaptions markets.

Essays on the Linkages Between Financial Markets, and Risk Asymmetries (summary section only)

During the last few decades there have been far going financial market deregulation, technical development, advances in information technology, and standardization of legislation between countries. As a result, one can expect that financial markets have grown more interlinked. The proper understanding of the cross-market linkages has implications for investment and risk management, diversification, asset pricing, and regulation. The purpose of this research is to assess the degree of price, return, and volatility linkages between both geographic markets and asset categories within one country, Finland. Another purpose is to analyze risk asymmetries, i.e., the tendency of equity risk to be higher after negative events than after positive events of equal magnitude. The analysis is conducted both with respect to total risk (volatility), and systematic risk (beta). The thesis consists of an introductory part and four essays. The first essay studies to which extent international stock prices comove. The degree of comovements is low, indicating benefits from international diversification. The second essay examines the degree to which the Finnish market is linked to the “world market”. The total risk is divided into two parts, one relating to world factors, and one relating to domestic factors. The impact of world factors has increased over time. After 1993, when foreign investors were allowed to freely invest in Finnish assets, the risk level has been higher than previously. This was also the case during the economic recession in the beginning of the 1990’s. The third essay focuses on the stock, bond, and money markets in Finland. According to a trading model, the degree of volatility linkages should be strong. However, the results contradict this. The linkages are surprisingly weak, even negative. The stock market is the most independent, while the money market is affected by events on the two other markets. The fourth essay concentrates on volatility and beta asymmetries. Contrary to many international studies there are only few cases of risk asymmetries. When they occur, they tend to be driven by the market-wide component rather than the portfolio specific element.

Essays on Risk and Return

Perhaps the most fundamental prediction of financial theory is that the expected returns on financial assets are determined by the amount of risk contained in their payoffs. Assets with a riskier payoff pattern should provide higher expected returns than assets that are otherwise similar but provide payoffs that contain less risk. Financial theory also predicts that not all types of risks should be compensated with higher expected returns. It is well-known that the asset-specific risk can be diversified away, whereas the systematic component of risk that affects all assets remains even in large portfolios. Thus, the asset-specific risk that the investor can easily get rid of by diversification should not lead to higher expected returns, and only the shared movement of individual asset returns – the sensitivity of these assets to a set of systematic risk factors – should matter for asset pricing. It is within this framework that this thesis is situated. The first essay proposes a new systematic risk factor, hypothesized to be correlated with changes in investor risk aversion, which manages to explain a large fraction of the return variation in the cross-section of stock returns. The second and third essays investigate the pricing of asset-specific risk, uncorrelated with commonly used risk factors, in the cross-section of stock returns. The three essays mentioned above use stock market data from the U.S. The fourth essay presents a new total return stock market index for the Finnish stock market beginning from the opening of the Helsinki Stock Exchange in 1912 and ending in 1969 when other total return indices become available. Because a total return stock market index for the period prior to 1970 has not been available before, academics and stock market participants have not known the historical return that stock market investors in Finland could have achieved on their investments. The new stock market index presented in essay 4 makes it possible, for the first time, to calculate the historical average return on the Finnish stock market and to conduct further studies that require long time-series of data.