Um curso na Universidade do Oregon, com o professor Sanjay Srivastava foi considerado por Cochrane como o programa mais hilário. O curso chama "Everything is Fucked".
Na introdução, o professor explica o que significa, na ciência, "to be fucked". O curso mostra situações onde algo está errado e que, por isto mesmo, representa um desafio. O conteúdo inclui, por exemplo, "psicologia is fucked".
27 julho 2019
Rir é o melhor remédio
26 julho 2019
Entrevista com Hoogervorst
a) medidas não GAAP
b) a questão da mudança no clima e seu reflexo na contabilidade
c) um provável estudo sobre o relatório da administração,
d) projeto de demonstrações financeiras primárias
Alguns destes tópicos já foram comentados neste blog. Um ponto importante é que Hoogervorst parece ainda não estar satisfeito com a contabilidade de seguradoras:
‘It has been clear that a lot of investors avoid investing in insurance companies because they cannot understand the accounting’, he says. ‘The existence of the new standard might draw more investors to the business of insurance’. At present he says that ‘it is a bit of a mess’.
Honestamente espero que o projeto do Iasb de se intrometer no Relatório da Administração não siga adiante. É necessário que a administração tenha liberdade para falar o que deseja nesta parte. Sem ter que cumprir "regrinhas" ditadas pelo regulador.
Aqui também. Eis o resumo do vídeo: Q1: Climate Change and financial risks. Q2: Climate-related financial disclosures. Q3: The Management Commentary Practice Statement. Q4: The potential impact of IBOR. Q5: The likely Changes to IFRS17. Q6: The benefits of the Primary Financial Statements project. Q7: The evolution of IFRS. Q8: Plans for the remaining years of Hoogervorst’s Chairmanship.
Precificação de ativos via Deep Learning
Resumo:
We propose a novel approach to estimate asset pricing models for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. Our general non-linear asset pricing model is estimated with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information. We estimate the stochastic discount factor that explains all asset returns from the conditional moment constraints implied by no-arbitrage. Our asset pricing model outperforms out-of-sample all other benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors. We trace its superior performance to including the no-arbitrage constraint in the estimation and to accounting for macroeconomic conditions and non-linear interactions between firm-specific characteristics. Our generative adversarial network enforces no-arbitrage by identifying the portfolio strategies with the most pricing information. Our recurrent Long-Short-Term-Memory network finds a small set of hidden economic state processes. A feedforward network captures the non-linear effects of the conditioning variables. Our model allows us to identify the key factors that drive asset prices and generate profitable investment strategies.
Fonte: Deep Learning in Asset Pricing (with L. Chen and J. Zhu)
We propose a novel approach to estimate asset pricing models for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. Our general non-linear asset pricing model is estimated with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information. We estimate the stochastic discount factor that explains all asset returns from the conditional moment constraints implied by no-arbitrage. Our asset pricing model outperforms out-of-sample all other benchmark approaches in terms of Sharpe ratio, explained variation and pricing errors. We trace its superior performance to including the no-arbitrage constraint in the estimation and to accounting for macroeconomic conditions and non-linear interactions between firm-specific characteristics. Our generative adversarial network enforces no-arbitrage by identifying the portfolio strategies with the most pricing information. Our recurrent Long-Short-Term-Memory network finds a small set of hidden economic state processes. A feedforward network captures the non-linear effects of the conditioning variables. Our model allows us to identify the key factors that drive asset prices and generate profitable investment strategies.
Fonte: Deep Learning in Asset Pricing (with L. Chen and J. Zhu)
Investidores não pensam em retorno percentual
Can the Market Multiply and Divide? Non-Proportional Thinking in Financial Markets
Kelly Shue e Richard Townsend, R&R Journal of Finance
First Prize AQR Insight Awards, 2019
Abstract: Nominal stock prices are arbitrary. Therefore, when evaluating how a piece of news should affect the price of a stock, rational investors should think in percentage rather than dollar terms. However, dollar price changes are ubiquitously reported and discussed. This may both cause and reflect a tendency of investors to think about the impact of news in dollar terms, leading to more extreme return responses to news for lower-priced stocks. We find a number of results consistent with such non-proportional thinking. First, lower-priced stocks have higher total volatility, idiosyncratic volatility, and market betas, after controlling flexibly for size. To identify a causal effect of price, we show that volatility increases sharply following pre-announced stock splits and drops following reverse stock splits. The returns of lower-priced stocks also respond more strongly to firm-specific news events, all else equal. The economic magnitudes are large: a doubling in a stock's nominal price is associated with a 20-30% decline in its volatility, beta, and return response to firm-specific news. These patterns are not exclusive to small, illiquid stocks; they hold even among the largest stocks. Non-proportional thinking can explain a variety of asset pricing anomalies such as long-run and short-run reversals, as well as the negative relation between past returns and volatility (i.e., the leverage effect). Our analysis also shows that the well-documented negative relation between risk (volatility or beta) and size is actually driven by nominal prices rather than fundamentals.
Kelly Shue e Richard Townsend, R&R Journal of Finance
First Prize AQR Insight Awards, 2019
Abstract: Nominal stock prices are arbitrary. Therefore, when evaluating how a piece of news should affect the price of a stock, rational investors should think in percentage rather than dollar terms. However, dollar price changes are ubiquitously reported and discussed. This may both cause and reflect a tendency of investors to think about the impact of news in dollar terms, leading to more extreme return responses to news for lower-priced stocks. We find a number of results consistent with such non-proportional thinking. First, lower-priced stocks have higher total volatility, idiosyncratic volatility, and market betas, after controlling flexibly for size. To identify a causal effect of price, we show that volatility increases sharply following pre-announced stock splits and drops following reverse stock splits. The returns of lower-priced stocks also respond more strongly to firm-specific news events, all else equal. The economic magnitudes are large: a doubling in a stock's nominal price is associated with a 20-30% decline in its volatility, beta, and return response to firm-specific news. These patterns are not exclusive to small, illiquid stocks; they hold even among the largest stocks. Non-proportional thinking can explain a variety of asset pricing anomalies such as long-run and short-run reversals, as well as the negative relation between past returns and volatility (i.e., the leverage effect). Our analysis also shows that the well-documented negative relation between risk (volatility or beta) and size is actually driven by nominal prices rather than fundamentals.
Registro de encontros com partes interessadas
O registro incluirá somente encontros com duração superior a 30 minutos, com informações sobre a data, nome da organização, localização e finalidade. A iniciativa decorreu de um relatório da Comissão Européia (copia aqui) que constatou que quando um membro da Fundação tinha encontros com stakeholders nenhum registro formal era mantido. É interessante que este documento "lembrava", no seu apêndice 2, que a Comissão Europeia era o segundo maior financiador da Fundação IFRS (após as Big Four).
É inocente supor que a mera divulgação dos encontros seja suficiente para impedir conflitos de interesse. Existe um velho ditado que diz "A mulher de César não basta ser honesta, deve parecer honesta".
Imagem: Pompeia, a mulher de César.
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