FINDING HAPPINESS IN THE LAW: LIFELONG LEARNING AS A PATH TO MEANING AND PURPOSE

We begin with the premise that the happiest and most fulfilled attorneys are those who live a life of meaning and purpose. While many in the legal profession have achieved this goal, many others are unsatisfied with their career trajectories but feel, for a variety of reasons, that they aren’t empowered to make a change. Unfortunately, study after study finds that many attorneys are stressed and unhappy with their professional lives, and would even leave the law entirely if they could. The Article argues that these attorneys have a far more dynamic set of options than simply leaving the profession or staying unhappy. It’s not just possible—but, for many attorneys, should be the goal—to merge personal and professional interests to achieve a career filled with meaning and purpose. The Article goes on to argue that lifelong learning is the path to achieving this goal. As every practitioner knows, law school taught us an analytical framework—how to “think like a lawyer”—which is reinforced in all aspects of legal practice. Relying on those analytical skills, it’s entirely possible to learn new practice areas, write on new topics, and continually evolve to ensure that one’s legal career aligns with personal goals. We say “continually evolve” because meaning and purpose change over time, so even the most fulfilled attorneys need to make adjustments throughout their careers to hit this mark. While these types of career adjustments certainly may entail significant transitions from one practice area to another, they more commonly entail smaller adjustments to one’s trajectory and current role that are minor in the moment but can have significant impact over time. The Article concludes with a discussion of the value of failure and rejection to achieving meaningful professional goals, and a reminder that regrets in old age often center on the things we didn’t do rather than the those we did. The goal of this Article is to reinforce to law students and attorneys that they need not sacrifice their values for a job, and it’s entirely possible to combine professional and personal goals in one’s career—and, indeed, doing so may very well lead to a happy and meaningful life

Foundations of Technical Analysis: Computational Algorithms, Statistical Inference, and Empirical Implementation

Technical analysis, also known as "charting", has been a part of financial practice for many decades, yet little academic research has been devoted to a systematic evaluation of this discipline. One of the main obstacles is the highly subjective nature of technical analysis---the presence of geometric shapes in historical price charts is often in the eyes of the beholder. In this paper, we propose a systematic and automatic approach to technical pattern recognition using nonparametric kernel regression, and apply this method to a large number of US stocks from 1962 to 1996 to evaluate the effectiveness of technical analysis. By comparing the unconditional empirical distribution of daily stock returns to the conditional distribution---conditioned on specific technical indicators such as head-and-shoulders or double-bottoms---we find that over the 31-year sample period, several technical indicators do provide incremental information and may have some practical value.

New News is Bad News

An increase in the novelty of news predicts negative stock market returns and negative macroeconomic outcomes over the next year. We quantify news novelty - changes in the distribution of news text - through an entropy measure, calculated using a recurrent neural network applied to a large news corpus. Entropy is a better out-of-sample predictor of market returns than a collection of standard measures. Cross-sectional entropy exposure carries a negative risk premium, suggesting that assets that positively covary with entropy hedge the aggregate risk associated with shifting news language. Entropy risk cannot be explained by existing long-short factors

Credit Information in Earnings Calls

We develop a novel technique to extract credit-relevant information from the text of quarterly earnings calls. This information is not spanned by fundamental or market variables and forecasts future credit spread changes. One reason for such forecastability is that our text-based measure predicts future credit spread risk and firm profitability. More firm- and call-level complexity increase the forecasting power of our measure for spread changes. Out-of-sample portfolio tests show the information in our measure is valuable for investors. Both results suggest that investors do not fully internalize the credit-relevant information contained in earnings calls

Asset Prices and Trading Volume Under Fixed Transactions Costs

We propose a dynamic equilibrium model of asset prices and trading volume with heterogeneous agents facing fixed transactions costs. We show that even small fixed costs can give rise to large 'no-trade' regions for each agent's optimal trading policy and a significant illiquidity discount in asset prices. We perform a calibration exercise to illustrate the empirical relevance of our model for aggregate data. Our model also has implications for the dynamics of order flow, bid/ask spreads, market depth, the allocation of trading costs between buyers and sellers, and other aspects of market microstructure, including a square-root power law between trading volume and fixed costs which we confirm using historical US stock market data from 1993 to 1997.

Relative performance evaluation contracts and asset market equilibrium

We analyse the equilibrium consequences of performance-based contracts for fund managers. Managerial remuneration is tied to a fund's absolute and relative performance. Investors choose whether or not to delegate their investment to better-informed fund managers; if they delegate they choose the optimal contract subject to the fund manager's participation constraint. We find that the impact of relative performance evaluation on the equilibrium equity premium and on portfolio herding critically depends on whether the participation constraint is binding. Simple numerical examples suggest that the increased importance of delegation and relative performance evaluation may lower the equity premium

Do mutual funds have consistency in their performance?

Using a comprehensive data set of 714 Chinese mutual funds from 2004 to 2015, the study investigates these funds’ performance persistence by using the Capital Asset Pricing model, the Fama-French three-factor model and the Carhart Four-factor model. For persistence analysis, we categorize mutual funds into eight octiles based on their one year lagged performance and then observe their performance for the subsequent 12 months. We also apply Cross-Product Ratio technique to assess the performance persistence in these Chinese funds. The study finds no significant evidence of persis- tence in the performance of the mutual funds. Winner (loser) funds do not continue to be winner (loser) funds in the subsequent time period. These findings suggest that future performance of funds cannot be predicted based on their past performance.info:eu-repo/semantics/publishedVersio

Stock Market Uncertainty and the Stock-Bond Return Relation

We examine whether time-variation in the co-movements of daily stock and Treasury bond returns can be linked to non-return-based measures of stock market uncertainty, specifically the implied volatility from equity index options and detrended stock turnover. From a forward-looking perspective, we find a negative relation between the uncertainty measures and the future correlation of stock and bond returns. From a contemporaneous perspective, we find that bond returns tend to be high (low), relative to stock returns, during days when implied volatility increases (decreases) substantially and during days when stock turnover is unexpectedly high (low). Our findings suggest that stock market uncertainty has important cross-market pricing influences and that stock-bond diversification benefits increase with stock market uncertaint

Mutual fund performance: Measurement and evidence

The paper provides a critical review of empirical findings on the performance of mutual funds, mainly for the US and UK. Ex-post, there are around 0-5% of top performing UK and US equity mutual funds with truly positive-alpha performance (after fees) and around 20% of funds that have truly poor alpha performance, with about 75% of active funds which are effectively zero-alpha funds. Key drivers of relative performance are, load fees, expenses and turnover. There is little evidence of successful market timing. Evidence suggests past winner funds persist, when rebalancing is frequent (i.e., less than one year) and when using sophisticated sorting rules (e.g., Bayesian approaches) - but transactions costs (load and advisory fees) imply that economic gains to investors from winner funds may be marginal. The US evidence clearly supports the view that past loser funds remain losers. Broadly speaking results for bond mutual funds are similar to those for equity funds. Sensible advice for most investors would be to hold low cost index funds and avoid holding past 'active' loser funds. Only sophisticated investors should pursue an active ex-ante investment strategy of trying to pick winners - and then with much caution. © 2010 New York University Salomon Center and Wiley Periodicals, Inc