[Transaction costs are important for a host of empirical analyses from market efficiency to international market research. But transaction costs estimates are not always available, or where available, are cumbersome to use and expensive to purchase. We present a model that requires only the time series of daily security returns to endogenously estimate the effective transaction costs for any firm, exchange, or time period. The feature of the data that allows for the estimation of transaction costs is the incidence of zero returns. Incorporating zero returns in the return-generating process, the model provides continuous estimates of average round-trip transaction costs from 1963 to 1990 that are 1.2% and 10.3% for large and small decile firms, respectively. These estimates are highly correlated (85%), with the most commonly used transaction cost estimators.]

David S. Kidwell, Charles A. Trzcinka, The Impact of the New York City Fiscal Crisis on the Interest Cost of New Issue Municipal Bonds, The Journal of Financial and Quantitative Analysis, Vol. 18, No. 3 (Sep., 1983), pp. 381-399

Strong‐form efficiency on the Toronto Stock Exchange is examined by focusing on the stock price forecasts of brokerage‐firm analysts who follow TSE firms. Two principal analyses are undertaken. First, there is considerable evidence in both the U.S. and U.K. that analysts possess valuable private information at the firm‐specific level. This paper provides evidence that this finding is generalizable to Canadian analysts. Second, U.S. and U.K. studies generally have been based on a single‐factor model (e.g., the CAPM). The choice of benchmarks (CAPM versus APT) has been shown to be important in a variety of contexts. We provide evidence that the choice of benchmark does not alter the fundamental conclusion that Canadian analysts possess valuable private information at the firm‐specific level. Our findings have implications for accounting researchers, namely, the appropriateness of researchers to use CAPM in lieu of the computationally, more burdensome APT and the appropriateness of researchers to use Canadian analyst forecasts when a proxy is required for the (unobservable) market expectation. Résumé. Les auteurs examinent l'efficience « forte » de la Bourse de Toronto, en s'intéressant aux prévisions relatives au prix des actions formulées par les analystes des sociétés de courtage qui suivent les entreprises de la Bourse de Toronto. Deux analyses principales les mènent aux conclusions suivantes. Premièrement, les faits démontrent presque indubitablement que sur le marché des États‐Unis aussi bien que sur celui du Royaume‐Uni, les analystes possèdent de l'information à caractère privé utile, spécifique à l'entreprise. Les résultats de l'étude démontrent que cette constatation peut être généralisée aux analystes canadiens. Deuxièmement, les études des États‐Unis et du Royaume‐Uni sont généralement fondées sur un modèle à un seul facteur (le modèle d'équilibre des marchés financiers, par exemple). Il a été établi que le choix des critères (le modèle d'équilibre des marchés financiers ou la théorie de l'établissement des prix par arbitrage) est important dans des circonstances très diverses. Les auteurs démontrent que le choix des critères n'a aucune incidence sur la conclusion fondamentale selon laquelle les analystes canadiens possèdent de l'information à caractère privé utile, spécifique à l'entreprise. Les résultats de leur étude entrainent certaines conséquences pour les chercheurs du domaine de la comptabilité: ils ont avantage à utiliser le modèle d'équilibre des marchés financiers de préférence à la théorie de l'établissement des prix par arbitrage, qui exige davantage de calculs, et à recourir aux prévisions des analystes canadiens lorsqu'il leur faut un substitut aux anticipations du marché (qui ne peuvent étre observées).

This paper's findings suggests that the New York City fiscal crisis by itself did not lead to a fundamental change in risk perceptions of investors, resulting in higher interest rates in the municipal bond market. The monthly prediction errors generated by time series tests were relatively small and none were statistically significant. Only the signs on the prediction errors for June, July, and August were consistent with a New York City effect. Thus, if the New York City default had an impact on aggregate interest rates, it was at most small and of short duration.

Given the key role of liquidity in finance research, identifying high quality proxies based on daily (as opposed to intraday) data would permit liquidity to be studied over relatively long timeframes and across many countries. Using new measures and widely employed measures in the literature, we run horseraces of annual and monthly estimates of each measure against liquidity benchmarks. Our benchmarks are effective spread, realized spread, and price impact based on both Trade and Quote (TAQ) and Rule 605 data. We find that the new effective/realized spread measures win the majority of horseraces, while the Amihud [2002. Illiquidity and stock returns: cross-section and time-series effects. Journal of Financial Markets 5, 31–56] measure does well measuring price impact.

Transaction costs are important for a host of empirical analyses from market efficiency to international market research. But transaction costs estimates are not always available, or where available, are cumbersome to use and expensive to purchase. We present a model that requires only the time series of daily security returns to endogenously estimate the effective transaction costs for any firm, exchange, or time period. The feature of the data that allows for the estimation of transaction costs is the incidence of zero returns. Incorporating zero returns in the return-generating process, the model provides continuous estimates of average round-trip transaction costs from 1963 to 1990 that are 1.2% and 10.3% for large and small decile firms, respectively. These estimates are highly correlated (85%), with the most commonly used transaction cost estimators.

Liquidity plays an important role in global research. We identify high-quality liquidity proxies based on low-frequency (daily) data, which provide 1,000× to 10,000× computational savings compared to computing high-frequency (intraday) liquidity measures. We find that: (i) Closing Percent Quoted Spread is the best monthly percent-cost proxy when available, (ii) Amihud, Closing Percent Quoted Spread Impact, LOT Mixed Impact, High–Low Impact, and FHT Impact are tied as the best monthly cost-per-dollar-volume proxy, (iii) the daily version of Closing Percent Quoted Spread is the best daily percent-cost proxy, and (iv) the daily version of Amihud is the best daily cost-per-dollar-volume proxy.