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This paper shows that ten methods on company valuation using cash flow discounting (WACC; equity cash flow; capital cash flow; adjusted present value; residual income; EVA; business's risk-adjusted equity cash flow; business's risk-adjusted free cash flow; risk-free-adjusted equity cash flow; and risk-free-adjusted free cash flow) always give the same value when identical assumptions are used. This result is logical, since all the methods analyze the same reality based upon the same assumptions; they only differ in the cash flows taken as the starting point for the valuation. We present all ten methods allowing the required return to debt being different from the cost of debt. Seven methods require an iterative process. Only APV and the business risk-adjusted cash flows methods do not require iteration.

Gold shows the characteristics of a zero-beta asset. It has approximately the same mean return as a Treasury Bill and bears no market risk. Silver also bears no market risk but has returns inferior to Treasury Bills. Both gold and silver show evidence of inflation-hedging ability, with the case being much stronger for gold. The prices of both metals are cointegrated with consumer prices, showing additional evidence of hedging ability.

Many problems in finance require the clustering of variables or observations. Despite its usefulness, clustering is almost never taught in Econometrics courses. In this seminar we review two general clustering approaches: partitional and hierarchical.

This paper assesses whether two popular accounting-based measures, Altman's (1968) Z-Score and an O-Score derived from Ohlson (1980), effectively summarize publicly-available information about the probability of bankruptcy (PB). According to option-pricing theories (Black and Scholes, 1973, Merton, 1974), a market-based measure, which we call BSM-PB, should reflect all available information about PB. These theories imply that not only will BSM-PB contain relatively more information than the Score variables, but that the accounting measures will not be incrementally informative to BSM-PB. We test the validity of these implications using a large sample consisting of 65,960 firm-year observations including 516 bankruptcies during the 1979-1997 period. Our statistical methodology utilizes a discrete hazard model that incorporates up to nineteen years of data per firm and produces unbiased coefficient estimates. Our main conclusion is that the traditional reliance on accounting-based measures of bankruptcy risk is inadequate. We find that BSM-PB has relatively more explanatory power than either of the two Scores, even when the Scores are decomposed to reflect industry differences or annual changes. However, the Scores contain significant, incremental information, and thus, BSM-PB is not a sufficient statistic for PB. Finally, we find that BSM-PB does not reflect all available market-based information regarding PB. Specifically, excess returns and relative market size provide incremental information.

This paper investigates the components of liquidity risk that are important for asset-pricing anomalies. Firm-level liquidity is decomposed into variable and fixed price effects and estimated using intraday data for the period 1983-2001. Unexpected systematic (market-wide) variations of the variable component rather than the fixed component of liquidity are shown to be priced within the context of momentum and post-earnings-announcement drift (PEAD) portfolio returns. As the variable component is typically associated with private information (e.g., Kyle (1985)), the results suggest that a substantial part of momentum and PEAD returns can be viewed as compensation for the unexpected variations in the aggregate ratio of informed traders to noise traders.

Empirical Finance is in crisis: Our most important "discovery" tool is historical simulation, and yet, most backtests published in leading Financial journals are flawed. The problem is well-known to professional organizations of Statisticians and Mathematicians, who have publicly criticized the misuse of mathematical tools among Finance researchers. In particular, reported results are not corrected for multiple testing. To this day, standard Econometrics textbooks seem oblivious to the issue of multiple testing. This may invalidate a large portion of the work done over the past 70 years. We present practical solutions to this problem. This presentation is related to papers http://ssrn.com/abstract=2308659, http://ssrn.com/abstract=2326253, http://ssrn.com/abstract=2460551, http://ssrn.com/abstract=2507040 and http://ssrn.com/abstract=2597421.

In this paper, we provide a detailed analysis of the Bitcoin network. We build a novel Bitcoin database and develop a methodology for identifying information about the main market participants. We conduct three major pieces of analysis that focus on the core functions of the new architecture: We document the transaction volumes and network structure of the main participants, the concentration and regional composition of miners which ensure the integrity of the blockchain ledger, and finally, the ownership concentration of the largest Bitcoin holders. We show that the Bitcoin eco-system is still dominated by large and concentrated players.

Controlling for unobserved heterogeneity (or “common errors”), such as industry-specific shocks, is a fundamental challenge in empirical research. This paper discusses the limitations of two approaches widely used in corporate finance and asset pricing research: demeaning the dependent variable with respect to the group (e.g., “industry-adjusting”) and adding the mean of the group’s dependent variable as a control. We show that these methods produce inconsistent estimates and can distort inference. In contrast, the fixed effects estimator is consistent and should be used instead. We also explain how to estimate the fixed effects model when traditional methods are computationally infeasible. Additional programming advice can be found on our websites.

By reaching a broad population of investors, mass media can alleviate informational frictions and affect security pricing even if it does not supply genuine news. We investigate this hypothesis by studying the cross-sectional relation between media coverage and expected stock returns. We find that stocks with no media coverage earn higher returns than stocks with high media coverage even after controlling for well-known risk-factors. These results are more pronounced among small stocks and stocks with high individual ownership, low analyst following, and high idiosyncratic volatility. Our findings suggest that the breadth of information dissemination affects stock returns.

Specified Purpose Acquisition Companies (SPACs) are a special type of public companies currently available to investors in financial markets. As an investment vehicle, modern SPACs are traced back to 18th century England where blank checks were first mentioned as blind pools during the infamous South Sea Bubble. In the United States, the Security and Exchange Commission classifies SPAC as a blank check company. This chapter reviews the academic and financial literatures about SPACS, describes their institutional characteristics and market performance since their Initial Public Offering (IPO). The sole purpose of SPACs is to use the proceeds to finance future acquisition.

We find that individual investors who use technical analysis and trade options frequently make poor portfolio decisions, resulting in dramatically lower returns than other investors. The data on which this claim is based consists of transaction records and matched survey responses of a sample of Dutch discount brokerage clients for the period 2000-2006. Overall, our results indicate that individual investors who report using technical analysis are disproportionately prone to have speculation on short-term stock-market developments as their primary investment objective, hold more concentrated portfolios which they turn over at a higher rate, are less inclined to bet on reversals, choose risk exposures featuring a higher ratio of nonsystematic risk to total risk, engage in more options trading, and earn lower returns.

We examine how local newspaper closures affect public finance outcomes for local governments. Following a newspaper closure, municipal borrowing costs increase by 5 to 11 basis points, costing the municipality an additional $650 thousand per issue. This effect is causal and not driven by underlying economic conditions. The loss of government monitoring resulting from a closure is associated with higher government wages and deficits, and increased likelihoods of costly advance refundings and negotiated sales. Overall, our results indicate that local newspapers hold their governments accountable, keeping municipal borrowing costs low and ultimately saving local taxpayers money.

Coronavirus (COVID-19) creates fear and uncertainty, hitting the global economy and amplifying the financial markets volatility. The oil price reaction to COVID-19 was gradually accommodated until March 09, 2020, when, 49 days after the release of the first coronavirus monitoring report by the World Health Organization (WHO), Saudi Arabia floods the market with oil. As a result, international prices drop with more than 20% in one single day. Against this background, the purpose of this paper is to investigate the impact of COVID-19 numbers on crude oil prices, while controlling for the impact of financial volatility and the United States (US) economic policy uncertainty. Our ARDL estimation shows that the COVID-19 daily reported cases of new infections have a marginal negative impact on the crude oil prices in the long run. Nevertheless, by amplifying the financial markets volatility, COVID-19 also has an indirect effect on the recent dynamics of crude oil prices.

Not all bubbles are born equal. We explore the nature and underlying psychology of four different kinds of bubbles, in order to assess which comes closest to describing the current market. To us, the current market environment is largely a greater fool market. Because such markets lack fundamental support, they are liable to precipitous declines. This is exacerbated when everyone seems to be watching the same indicator (earnings optimism). As Keynes noted when disillusion falls upon an over-optimistic and over-bought market, it should fall with sudden and catastrophic force.

Firm size has been empirically found to be strongly positively related to capital structure. A number of intuitive explanations can be put forward to account for this stylized fact, but none have been considered theoretically. This paper starts bridging this gap by investigating whether a dynamic capital structure model can explain the cross-sectional size-leverage relationship. The driving force that we consider is the presence of fixed costs of external financing that lead to infrequent restructuring and create a wedge between small and large firms. We find four firm size effects on leverage. Small firms choose higher leverage at the moment of refinancing to compensate for less frequent rebalancings. But longer waiting times between refinancings lead on average to lower levels of leverage. Within one refinancing cycle the intertemporal relationship between leverage and firm size is negative. Finally, there is a mass of firms opting for no leverage. The analysis of dynamic economy demonstrates that in cross-section the relationship between leverage and size is positive and thus fixed costs of financing contribute to the explanation of the stylized size-leverage relationship. However, the relationship changes the sign when we control for the presence of unlevered firms.

Psychological evidence and casual intuition predict that sunny weather is associated with upbeat mood. This paper examines the relation between morning sunshine at a country's leading stock exchange and market index stock returns that day at 26 stock exchanges internationally from 1982-97. Sunshine is strongly positively correlated with daily stock returns. After controlling for sunshine, other weather conditions such as rain and snow are unrelated to returns. If transactions costs are assumed to be minor, it is possible to trade profitably on the weather. These results are difficult to reconcile with fully rational price-setting.

A turn-of-the-month effect in U.S. equity returns was initially identified by Lakonishok and Smidt (1988) using the DJIA for the period 1897-1986. According to the turn-of-the-month effect, equity returns over the interval beginning the last trading day of the month and ending three days later are significantly higher than over other days. Using CRSP daily returns, we find that the turn-of-the-month effect persists over the recent interval of 1987-2005: in essence, over this 19-year period (and over the 109-year period of 1897-2005) all of the excess market return occurred during the four-day turn-of-the-month interval. Thus, during the other 16 trading days of the month, on average, investors received no reward for bearing market risk. We further find that the turn-of-the-month effect is not confined to small or low-priced stocks; it is not confined to the December-January turn-of-the-month; it is not confined to calendar-quarter-ends; it is not confined to the U.S.; and it is not due to market risk as traditionally measured: the standard deviation of returns at the turn-of-the-month is no higher than during other days. This persistent peculiarity in equity returns poses a challenge to both "rational" and "behavioral" models of asset pricing.

Prior research and empirical investment results have shown that portfolio construction choices related to rebalance schedules may have non-trivial impacts on realized performance. We construct long-only indices that provide exposures to popular U.S. equity factors (value, size, momentum, quality, and low volatility) and vary their rebalance schedules to isolate the effects of “rebalance timing luck.” Our constructed indices exhibit high levels of rebalance timing luck, often exceeding 100 basis points annualized, with total impact dependent upon the frequency of rebalancing, portfolio concentration, and the nature of the underlying strategy. As a case study, we replicate popular factor-based index funds and similarly find meaningful performance impacts due to rebalance timing luck. For example, a strategy replicating the S&P Enhanced Value index saw calendar year return differentials above 40% strictly due to the rebalance schedule implemented. Our results suggest substantial problems for analyzing any investment when the strategy, its peer group, or its benchmark is susceptible to performance impacts driven by the choice of rebalance schedule.

This paper undertakes a comparative study of portfolio insurance under a variety of modelling strategies. Specifically, we focus on portfolio insurers who drive utility from horizon wealth, with marginal utility tending smoothly to infinity at some pre-specified floor. We solve for the optimal consumption-portfolio-wealth of these portfolio insurers and compare with "constrained" portfolio insurers and "normal agents." General equilibrium conditions are contrasted under pure-exchange and production-type models. While the market price level is unambiguously increased under pure-exchange, under production the effect on market level is state-dependent. In both models the market volatility and risk premium are decreased by portfolio insurance The paper also investigates the possible relationship between portfolio insurance type trading strategies and market volatility.

We find that deviations from the covered interest rate parity condition (CIP) imply large, persistent, and systematic arbitrage opportunities in one of the largest asset markets in the world. Contrary to the common view, these deviations for major currencies are not explained away by credit risk or transaction costs. They are particularly strong for forward contracts that appear on the banks' balance sheets at the end of the quarter, pointing to a causal effect of banking regulation on asset prices. The CIP deviations also appear significantly correlated with other fixed-income spreads and with nominal interest rates.

Machine learning (ML) algorithms utilize the power of computers to solve tasks that are beyond the grasp of classical statistical methods. However, ML is often perceived as a black-box, hindering its adoption. This seminar demonstrates the use of Shapley values to interpret the outputs of ML models. With the help of interpretability methods, ML is becoming the primary tool of scientific discovery, through induction as well as abduction.

Using a sample of 97 stock return anomalies, we find that anomaly returns are 50% higher on corporate news days and are 6 times higher on earnings announcement days. These results could be explained by dynamic risk, mispricing via biased expectations, and data mining. We develop and conduct unique tests to differentiate between these three frameworks. Our results are most consistent with the idea that anomaly returns are the result of biased expectations, which are at least partially corrected upon news arrival.

This paper begins with an overview of the Indian insurance market in Section II, which highlights the phenomenal growth experienced recently, in line with the country's improving economic fundamentals. Section III benchmarks the Indian insurance market against other regional counterparts. By comparing growth, penetration, density and other insurance variables, it can be shown that, whilst India is still an underdeveloped insurance market, it has a huge catch-up potential. Section IV presents a necessary overview of the historical development of the sector, but the relevance to the current marketplace is not lost, as the original 1938 Insurance Act still forms the backbone of present insurance regulation. A more detailed dissection of current regulatory issues is offered in Section V. Sections VI and VII discuss issues in the life and non-life insurance sectors respectively. Developments with far-reaching implications, like the proliferation of bancassurance as an alternative distribution channel and the move to allow non-life insurance companies greater freedom in pricing their products, are looked at in detail. Finally, Section VIII summarises the potential and pitfalls of rural insurance in India. Even though there is strong potential for expansion of insurance into rural areas, growth has so far remained slow. Considering that the bulk of the Indian population still resides in rural areas, it is imperative that the insurance industry's development should not miss this vast sector of the population.

When used incorrectly, the risk of machine learning (ML) overfitting is extremely high. However, ML counts with sophisticated methods to prevent: (a) train set overfitting, and (b) test set overfitting.Thus, the popular belief that ML overfits is false. A more accurate statement would be that: (1) in the wrong hands, ML overfits, and (2) in the right hands, ML is more robust to overfitting than classical methods.When it comes to modelling unstructured data, ML is the only choice. Classical statistics should be taught as a preparation for ML courses, with a focus on overfitting prevention.

Robo-advisors promise efficient, rational, and transparent investment advisory. We analyze how robo-advisors determine their users' risk tolerance and which equity exposure is derived from the individual risk profile. Our findings indicate significant differences in the quality of offered investment advice. Robo-advisors usually ask relatively few questions in the assessment of their users' risk profile, and it is particularly surprising that some of the questions do not seem to have any impact on the risk categorization. Moreover, the recommended equity exposure is relatively conservative.