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A journey through the Index Revolution from the man who started it all Stay the Course is the story the Vanguard Group as told by its founder, legendary investor John C. Bogle. This engrossing book traces the history of Vanguard—the largest mutual fund organization on earth. Offering the world's first index mutual fund in 1976, John Bogle led Vanguard from a $1.4 billion firm with a staff of 28 to a global company of 16, 000 employees and with more than $5 trillion in assets under management. An engaging blend of company history, investment perspective, and personal memoir, this book provides a fascinating look into the mind of an extraordinary man and the company he created. John Bogle continues to be an inspiring and trusted figure to millions of individual investors the world over. His creative innovation, personal integrity, and stubborn determination infuse every aspect of the company he founded. This accessible and engaging book will help you: * Explore the history of some of Vanguard's most important mutual funds, including First Index Investment Trust, Wellington Fund, and Windsor Fund * Understand how the Vanguard Group gave rise to the Index Revolution and transformed the lives of millions of individual investors * Gain insight on John Bogle's views on values such as perseverance, caring, commitment, integrity, and fairness * Investigate a wide range of investing topics through the lens of one of the most prominent figures in the history of modern finance The Vanguard Group and John Bogle are inextricably linked—it would be impossible to tell one story without the other. Stay the Course: The Story of Vanguard and the Index Revolution weaves these stories together taking you on a journey through the history of one revolutionary company and one remarkable man. Investors, wealth managers, financial advisors, business leaders, and those who enjoy a good story, will find this book as informative and unique as its author.

From 1997 to March 2000, as technology stocks rose more than five-fold, institutions bought more new technology supply than individuals. Among institutions, hedge funds were the most aggressive investors, but independent investment advisors and mutual funds (net of flows) actively invested the most capital in the technology sector. The technology stock reversal in March 2000 was accompanied by a broad sell-off from institutional investors but accelerated buying by individuals, particularly discount brokerage clients. Overall, our evidence supports the bubble model of Abreu and Brunnermeier (2003), in which rational arbitrageurs fail to trade against bubbles until a coordinated selling effort occurs.

Following a review of the data and methodological difficulties in applying conventional models used for traditional asset class indexes to hedge funds, this article argues against the conventional approach. Instead, in an extension of previous work on asset-based style (ABS) factors, the article proposes a model of hedge fund returns that is similar to models based on arbitrage pricing theory, with dynamic risk-factor coefficients. For diversified hedge fund portfolios (as proxied by indexes of hedge funds and funds of hedge funds), the seven ABS factors can explain up to 80 percent of monthly return variations. Because ABS factors are directly observable from market prices, this model provides a standardized framework for identifying differences among major hedge fund indexes that is free of the biases inherent in hedge fund databases. Conventional models for constructing asset class indexes rest on the assumptions that the underlying assets have homogeneous performance characteristics and that the dominant investment strategy is to buy and hold assets. In contrast, the performance characteristics of hedge funds are diverse, the investment styles are dynamic, and bets may be highly levered. These hedge fund characteristics, together with the lack of standardized reporting of historical hedge fund performance, greatly limit the information content of hedge fund indexes that are constructed by using conventional methods. At times, using such indexes can even produce misleading results. In the study reported here, we used a method to create hedge fund benchmarks that captures the common risk factors in hedge funds by using asset-based style (ABS) factors. Model construction began by extracting common sources of risk from hedge fund returns. These sources of risk were identified by directly linking them to various market risk factors. These ABS factors were then used to construct a hedge fund risk-factor model similar to the approach in arbitrage pricing theory, in which the factor loadings (betas) are permitted to vary over time. Thus far, researchers have identified seven risk factors. Equity long-short hedge funds are exposed to two equity risk factors-market risk (as proxied by the S&P 500 Index) and the spread between small-capitalization stock returns and large-capitalization stock returns. Fixed-income hedge funds are exposed to two interest-rate-related risk factors-the change in 10-year U.S. Treasury yields and the change in the yield spread between 10-year T-bonds and Moody's Investors Service Baa bonds. Trend-following funds are exposed to the same risk factors as three portfolios of \"lookback\" options-on bond futures, on currency futures, and on commodity futures. Empirical evidence shows that these seven risk factors can jointly explain a major portion of return movements in diversified hedge fund portfolios, as proxied by a fund-of-funds index. Applying the risk-factor model to hedge fund indexes, we show that the model can identify risk differences inherent in these indexes, which in turn, helps explain anomalous return differences among them. An out-of-sample check on the usefulness of the risk-factor model with 2003 data indicates that the model explains a significant amount of the return differences among major hedge fund indexes. The ABS risk-factor model can be applied to circumvent the lack of transparency in hedge fund investments. It helps investors relate hedge fund strategies to a set of common risk factors, which can be key inputs for portfolio construction, risk management, and performance evaluation. Because ABS factors are measured in market prices, investors can frequently approximate the performance of their hedge fund investments to match the changing conditions of global markets without having to rely on normal net-asset-value reporting as the only input. Hedge fund managers can also use ABS factors to communicate the systematic risk inherent in a strategy to investors in a format that is consistent with the qualitative description of the strategy's style. Thus, risk disclosure and transparency can be brought to a satisfactory aggregated level without having to analyze the huge volume of individual hedge fund transactions. The same framework can be used by regulators to monitor aggregate exposures to systematic risks. This use would provide important input to the management of stressful events, such as the bond market decline of 1994.

The average nominal share prices of common stocks traded on the New York Stock Exchange have remained constant at approximately$35 per share since the Great Depression as a result of stock splits. It is surprising that U.S. firms actively maintained constant nominal prices for their shares while general prices in the economy went up more than tenfold. This is especially puzzling given that commissions paid by investors on trading ten $ 35 shares are about ten times those paid on a single $350 share. We review potential explanations including signaling and optimal trading ranges and find that none of the existing theories are able to explain the observed constant nominal prices. We suggest that the evidence is consistent with the idea that customs and norms can explain the nominal price puzzle.

This thesis aims to understanding the hedge fund trading behaviors, including stock picking, stock price manipulation, and their impact on stock market. It consists of three chapters, which are independent research papers.In the first chapter, we investigate who the counterparties of hedge fund equity trading are and what the economic reasons behind their trading decisions might be. We find that hedge funds earn positive ex-post abnormal returns and avoid negative abnormal returns on their equity portfolios when trading in the opposite direction of highly-diversified low-turnover institutional investors (quasi-indexers). This pattern is pronounced for short and long-term holding periods, as well as if trading is conditional on return predictability associated with well-known market anomalies. It seems to be driven by the preferences of quasi-indexers for liquid, high-market-beta stocks, which tend to exhibit low future abnormal returns. Trading against other institutional investors or non-institutions does not result in abnormal performance for hedge funds.In the second chapter, we analyze the equity trading of hedge funds facing substantial outflows. We find that hedge funds that trade against the flow display significant stock-picking skills. Stocks purchased by hedge funds facing large outflows deliver positive ex-post abnormal returns. Such â€oerevealed under pressure” stock-picking skills are higher after 2007-2008 financial crisis and for hedge funds with larger size, more illiquid assets, or stronger incentives to perform to build up a track record. We also find that hedge funds that engage in the trading against the flow have higher chances of survival over the consequent quarter.In the third chapter, we investigate the stock manipulation of hedge funds. We follow a research paper published in The Journal of Finance (Ben-David et al., 2013) presenting empirical evidence of stock price manipulation by hedge funds between 2000 and 2010. They show that stocks held by hedge funds exhibited positive daily abnormal returns and then reversals (â€oeblips”) at quarter end. These results are cross-sectionally robust: we replicate them using a different sample of hedge funds during the same time period. In the post-publications period from 2011 to 2018, however, we find no significant relation between hedge fund ownership and end-of-quarter stock returns, suggesting reduction in stock price manipulation by hedge funds post-publication.We is for first person and he/she is for third person throughout the thesis to indicate that three chapters are co-authored with my supervisor Olga Kolokolova (and with George Wang for the first chapter). The empirical analysis in all chapters is my own work, while we equally contributed with the co-authors to the development of the idea, discussion of methodology, and structuring of the papers.

We evaluate the performance of 51 mutual fund families based on a study of their diversified US managed mutual funds over an 11-year-period and explore the determinants of performance gross of published expenses. We find that mutual fund families which charge loads, high expenses to their most favored investors and have high turnover tend to perform badly, even gross of these fees. However, gross of published expenses, managed mutual fund portfolios of those families without loads, with low expenses in their least expensive class, and with low average turnover beat the corresponding indexes.

The Europe and Central Asia (ECA) region has been hit by a crisis on multiple fronts. Countries in ECA are facing major, interrelated, external macro-financial shocks. The first is the global growth slowdown leading to falling export market demand. In addition, the prospects for inflows of remittances to low-income countries have been downgraded as economic activity in migrant host countries has declined. The second is the financial deleveraging by major banks and other financial institutions in developed economies, which has markedly reduced the availability, and increased the cost, of external finance across public, corporate, and financial sectors. The third is the recent commodity price changes, which have involved a reversal of much of the commodity price boom of 2007 and 2008. The main objective of the study is to understand the impact of these macroeconomic shocks on household well-being. In particular, it seeks to understand the key macroeconomic shocks confronted by the region and the impact of such shocks on household welfare, including the effect on household income flows, consumption levels, and liabilities. It will also assess possible strategies to cope with the crisis and manage the adverse social impact.

Evolve Fund Ticker/ Fund Code Previous Risk Rating New Risk Rating Evolve Cyber Security Index Fund – USD Unhedged ETF Units (US$) CYBR.U Medium to High High Evolve Future Leadership Fund – USD Unhedged ETF Units (US$) LEAD.U Medium Medium to High Evolve NASDAQ Technology Enhanced Yield Index Fund – Hedged ETF Units QQQY Medium Medium to High Evolve NASDAQ Technology Enhanced Yield Index Fund – Unhedged ETF Units QQQY.B Medium Medium to High Evolve NASDAQ Technology Enhanced Yield Index Fund – USD Unhedged ETF Units (US$) QQQY.U Medium Medium to High Evolve NASDAQ Technology Enhanced Yield Index Fund – Hedged Class F Mutual Fund Units EVF900 Medium Medium to High Evolve NASDAQ Technology Enhanced Yield Index Fund – Hedged Class A Mutual Fund Units EVF901 Medium Medium to High The investment risk level of an ETF and mutual fund (the \"Fund\") is determined in accordance with a standardized risk classification methodology, set out in National Instrument 81-102 Investment Funds, that is based on the historical volatility of the Fund, as measured by the 10-year standard deviation of the returns of the Fund. If a Fund has less than 10 years of performance history, the investment risk level of the Fund is calculated using the return history of the Fund, and, for the remainder of the 10-year period, the return history of a reference index that is expected to reasonably approximate the standard deviation of the Fund. About Evolve Funds Group Inc. With $7 billion in assets under management, Evolve is one of Canada's fastest growing ETF providers since launching its first ETF in September 2017.