We propose a theory of securities market under- and overreactions based on two well-known psychological biases: investor overconfidence about the precision of private information; and biased self-attribution, which causes asymmetric shifts in investors' confidence as a function of their investment outcomes. We show that overconfidence implies negative long-lag autocorrelations, excess volatility, and, when managerial actions are correlated with stock mispricing, public-event-based return predictability. Biased self-attribution adds positive short-lag autocorrelations (momentum), short-run earnings drift, but negative correlation between future returns and long-term past stock market and accounting performance. The theory also offers several untested implications and implications for corporate financial policy.
Prepublication version available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2017
A brief understanding of market efficiency. this ppt includes a definition of market efficiency, what are the factors to be considered, degree of ME-
first-degree,
second degree
third degree,
why the study of market efficiency is important.
An example to understand.
Defined Expected utility theory,
Defined Prospect Theory,
Defined Disposition effect
Defined Heuristics and biases
Contact: rehankango@ymail.com +92337548656
Abstract
The idea of an Efficient Market first came from the French mathematician Louis Bachelier in 1900: « The theory of speculation ».
Bachelier argued that there is no useful information in past stock prices that can help predicting future prices and proposed a theory for financial options’ valuation based on Fourier’s law and Brownian’s motions (time series).
Bachelier’s work get popular in the 60s during the computer’s era.
In 1965, Eugene Fama published a dissertation arguing for the random walk hypothesis (Stock market’s prices evolve randomly: prices cannot be predicted using past data).
In 1970, Fama published a review of the theory and empirical evidences
The EMH (Efficient Market Hypothesis): Financial markets are efficient at processing information. Consequently, the prices of securities is a correct representation of all information available at any time.
Weak:
Not possible to earn superior profits (risk adjusted) based on the knowledge of past prices and returns.
Semi-strong:
Not possible to earn superior profits using all information publicly available.
Strong:
Not possible to earn superior profit using all publicly and inside information.
The CAPM describes the relationship between market risks and expected return for a security i (also called cost of equity), E(Re_i):
Re_i = Rf – Bi(Rm – Rf)
With:
Rf = Risk free rate (typically government bond rate)
Rm = Expected return for the whole market
Bi = The volatility risk of the security i compared to the whole market
(Rm – Rf) is consequently the market risk premium
According to the EMH, for a well-diversified portfolio, expected returns can only reflect those of the market as a whole. Consequently, in the CAPM formula, It would involves that for a diversified-enough portfolio: β = 1 so Re = Rm
Investors want to value companies before making investment decisions.
A typical way to do so is to use the Discounted Cash Flow (DCF) method:
See also: Prospect theory, disposition effect, heuristic, framing, mental accounting, Home bias, representativeness, conservatism, availability, greater fool theory, self attribution theory, anchoring, ambiguity aversion, winner's curse, managerial miscalibration and misconception, Equity premium puzzle, market anomalies, excess volatility, Bubbles, herding, limited liabilities, Fama French three 3 factors model.
A brief understanding of market efficiency. this ppt includes a definition of market efficiency, what are the factors to be considered, degree of ME-
first-degree,
second degree
third degree,
why the study of market efficiency is important.
An example to understand.
Defined Expected utility theory,
Defined Prospect Theory,
Defined Disposition effect
Defined Heuristics and biases
Contact: rehankango@ymail.com +92337548656
Abstract
The idea of an Efficient Market first came from the French mathematician Louis Bachelier in 1900: « The theory of speculation ».
Bachelier argued that there is no useful information in past stock prices that can help predicting future prices and proposed a theory for financial options’ valuation based on Fourier’s law and Brownian’s motions (time series).
Bachelier’s work get popular in the 60s during the computer’s era.
In 1965, Eugene Fama published a dissertation arguing for the random walk hypothesis (Stock market’s prices evolve randomly: prices cannot be predicted using past data).
In 1970, Fama published a review of the theory and empirical evidences
The EMH (Efficient Market Hypothesis): Financial markets are efficient at processing information. Consequently, the prices of securities is a correct representation of all information available at any time.
Weak:
Not possible to earn superior profits (risk adjusted) based on the knowledge of past prices and returns.
Semi-strong:
Not possible to earn superior profits using all information publicly available.
Strong:
Not possible to earn superior profit using all publicly and inside information.
The CAPM describes the relationship between market risks and expected return for a security i (also called cost of equity), E(Re_i):
Re_i = Rf – Bi(Rm – Rf)
With:
Rf = Risk free rate (typically government bond rate)
Rm = Expected return for the whole market
Bi = The volatility risk of the security i compared to the whole market
(Rm – Rf) is consequently the market risk premium
According to the EMH, for a well-diversified portfolio, expected returns can only reflect those of the market as a whole. Consequently, in the CAPM formula, It would involves that for a diversified-enough portfolio: β = 1 so Re = Rm
Investors want to value companies before making investment decisions.
A typical way to do so is to use the Discounted Cash Flow (DCF) method:
See also: Prospect theory, disposition effect, heuristic, framing, mental accounting, Home bias, representativeness, conservatism, availability, greater fool theory, self attribution theory, anchoring, ambiguity aversion, winner's curse, managerial miscalibration and misconception, Equity premium puzzle, market anomalies, excess volatility, Bubbles, herding, limited liabilities, Fama French three 3 factors model.
Behavioral finance and investment decisionaashima1806
Behavioral Finance is all related to the behavior of the investor at the time of investing in different market conditions.. same is exhibited in our presentation by compiling different questions related to investment for different investors on the basis of different age groups...
This is a Behavioral Finance Lesson material which delivered by me for PhD students of Faculty of Business Administration in Karvina, Silesian University.
Behavioural Finance - An Introspection Of Investor PsychologyTrading Game Pty Ltd
Investors always try to make rational decision while analyzing and interpreting information collected from various sources for different investment avenues to arrive at an optimal investment decision. But at the same time they are influenced by various psychological factors that influence them internally and bias their investment decision. Linter (1998) studied the various factors that influence internally the informed investment decision and included them under the discipline of behavioural finance. Behavioural finance studies how people make investment decision and influenced by internal factors and bias. The main purpose of the paper is to assess impact of behavioural factors over mutual fund investment decision made by investors in Raipur city.
Behavioral Finance key notes for non financial managers. This help also the financial advisors discover the type of behavioral finance biases among their clients.
It also highlight the types of financial risks, and types of clients according to their risk capacity and risk tolerance.
It also add value to investors specially in the investment decision making process.
Limited Attention, Information Disclosure, and Financial ReportingDavid Hirshleifer
We model firms' choices between alternative means of presenting information, and the effects of different presentations on market prices when investors have limited attention and processing power. In a market equilibrium with partially attentive investors, we examine the effects of alternative: levels of discretion in pro forma earnings disclosure, methods of accounting for employee option compensation, and degrees of aggregation in reporting. We derive empirical implications relating pro forma adjustments, option compensation, the growth, persistence, and informativeness of earnings, short-run managerial incentives, and other firm characteristics to stock price reactions, misvaluation, long-run abnormal returns, and corporate decisions.
The paper is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=334940
Behavioral finance and investment decisionaashima1806
Behavioral Finance is all related to the behavior of the investor at the time of investing in different market conditions.. same is exhibited in our presentation by compiling different questions related to investment for different investors on the basis of different age groups...
This is a Behavioral Finance Lesson material which delivered by me for PhD students of Faculty of Business Administration in Karvina, Silesian University.
Behavioural Finance - An Introspection Of Investor PsychologyTrading Game Pty Ltd
Investors always try to make rational decision while analyzing and interpreting information collected from various sources for different investment avenues to arrive at an optimal investment decision. But at the same time they are influenced by various psychological factors that influence them internally and bias their investment decision. Linter (1998) studied the various factors that influence internally the informed investment decision and included them under the discipline of behavioural finance. Behavioural finance studies how people make investment decision and influenced by internal factors and bias. The main purpose of the paper is to assess impact of behavioural factors over mutual fund investment decision made by investors in Raipur city.
Behavioral Finance key notes for non financial managers. This help also the financial advisors discover the type of behavioral finance biases among their clients.
It also highlight the types of financial risks, and types of clients according to their risk capacity and risk tolerance.
It also add value to investors specially in the investment decision making process.
Limited Attention, Information Disclosure, and Financial ReportingDavid Hirshleifer
We model firms' choices between alternative means of presenting information, and the effects of different presentations on market prices when investors have limited attention and processing power. In a market equilibrium with partially attentive investors, we examine the effects of alternative: levels of discretion in pro forma earnings disclosure, methods of accounting for employee option compensation, and degrees of aggregation in reporting. We derive empirical implications relating pro forma adjustments, option compensation, the growth, persistence, and informativeness of earnings, short-run managerial incentives, and other firm characteristics to stock price reactions, misvaluation, long-run abnormal returns, and corporate decisions.
The paper is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=334940
Since the CAPM model Sharpe (1965) and the first “fundamental” model by King (1966) the use of “factors” in alpha generation and risk modeling has become mainstream. However, the types of factors we employ and the techniques we use to model relationships have in general not progressed much since. In addition, many of our favorite techniques assume that the world is static, whereas of course markets evolve and change dramatically; as we have seen so vividly illustrated over the last few years.
We review fundamental, macro-economic, and statistical factors, describing the advantages and disadvantages of each, and review some newer techniques that explicitly allow for evolving relationships in data sets and harness emerging technologies that can capture much more nuanced relationships than simple correlation: “flexible” least-squares regression, artificial immune systems, single-pass clustering, semantic clustering, social network influence measurement, layer-embedded networks, block-modeling, and more.
A review of the assumptions behind fundamental, macro, and statistical risk models. Pros and cons of each approach. Introducing adaptive hybrid risk models.
* Corresponding author. Tel.: 773 702 7282; fax: 773 702 9937; e-mail: [email protected]
edu.
1 The comments of Brad Barber, David Hirshleifer, S.P. Kothari, Owen Lamont, Mark Mitchell,
Hersh Shefrin, Robert Shiller, Rex Sinquefield, Richard Thaler, Theo Vermaelen, Robert Vishny, Ivo
Welch, and a referee have been helpful. Kenneth French and Jay Ritter get special thanks.
Journal of Financial Economics 49 (1998) 283—306
Market efficiency, long-term returns, and behavioral
finance1
Eugene F. Fama*
Graduate School of Business, University of Chicago, Chicago, IL 60637, USA
Received 17 March 1997; received in revised form 3 October 1997
Abstract
Market efficiency survives the challenge from the literature on long-term return
anomalies. Consistent with the market efficiency hypothesis that the anomalies are
chance results, apparent overreaction to information is about as common as underreac-
tion, and post-event continuation of pre-event abnormal returns is about as frequent as
post-event reversal. Most important, consistent with the market efficiency prediction that
apparent anomalies can be due to methodology, most long-term return anomalies tend to
disappear with reasonable changes in technique. ( 1998 Elsevier Science S.A. All rights
reserved.
JEL classification: G14; G12
Keywords: Market efficiency; Behavioral finance
1. Introduction
Event studies, introduced by Fama et al. (1969), produce useful evidence on
how stock prices respond to information. Many studies focus on returns in
a short window (a few days) around a cleanly dated event. An advantage of this
approach is that because daily expected returns are close to zero, the model for
expected returns does not have a big effect on inferences about abnormal returns.
0304-405X/98/$19.00 ( 1998 Elsevier Science S.A. All rights reserved
PII S 0 3 0 4 - 4 0 5 X ( 9 8 ) 0 0 0 2 6 - 9
The assumption in studies that focus on short return windows is that any lag
in the response of prices to an event is short-lived. There is a developing
literature that challenges this assumption, arguing instead that stock prices
adjust slowly to information, so one must examine returns over long horizons to
get a full view of market inefficiency.
If one accepts their stated conclusions, many of the recent studies on long-
term returns suggest market inefficiency, specifically, long-term underreaction
or overreaction to information. It is time, however, to ask whether this litera-
ture, viewed as a whole, suggests that efficiency should be discarded. My answer
is a solid no, for two reasons.
First, an efficient market generates categories of events that individually
suggest that prices over-react to information. But in an efficient market, appar-
ent underreaction will be about as frequent as overreaction. If anomalies split
randomly between underreaction and overreaction, they are consistent with
market efficiency. We shall see that a roughly even split between apparent
overreaction and underreact ...
Taking on Wall Street: A Comparative Study of Strategies Sourced from "The Pr...Quantopian
A unique set of data comprised of strategy returns sourced through traditional means from managers (“the pros”) and from strategies developed on Quantopian’s platform (“the crowd”) is analyzed. We detect distinct groups of strategy styles within the data: In particular, some "crowd" strategies fall into their own clusters distinct from those within the "pro" data set. A few do overlap as well. We go on to analyze the various strategy groups with respect to environmental conditions and risk factors (among other relevant features), teasing out differences in trading styles.
Ultimately we judge how well “the crowd” is doing so far, in terms of being able to compete with the established managers not only in terms of performance but also with respect to risk management and overall novelty and diversification in the trading styles that have emerged. Finally we address general notions (and pitfalls) of building meta strategies from manager return streams.
This presentation was part of QuantCon 2015 hosted by Quantopian. Visit us at: www.quantopian.com.
Application of ML / DL in Finance / InvestmentJIEJackyZOUChou
A presentation on Application of Machine Learning / Deep Learning in Finance / Investment. This is based off a survey short story that was posted on Medium: https://medium.com/@jie.zou/application-of-machine-learning-deep-learning-in-finance-investment-30f744e55551
Application of ML / DL in Finance / InvestmentJIEJackyZOUChou
A presentation on Application of Machine Learning / Deep Learning in Finance / Investment. This is based off a survey short story that was posted on Medium: https://medium.com/@jie.zou/application-of-machine-learning-deep-learning-in-finance-investment-30f744e55551
The basic paradigm of asset pricing is in vibrant flux. The purely rational approach is being subsumed by a broader approach based upon the psychology of investors. In this approach, security expected returns are determined by both risk and misvaluation. This survey sketches a framework for understanding decision biases, evaluates the a priori arguments and the capital market evidence bearing on the importance of investor psychology for security prices, and reviews recent models.
Prepublication version of paper: https://ssrn.com/abstract=265132.
We document strong persistence in the performance of trades of individual investors. The correlation of the risk-adjusted performance of an individual across sample periods is about 10 percent. Investors classified in the top performance decile in the first half of our sample subsequently outperform those in the bottom decile by about 8 percent per year. Strategies long in firms purchased by previously successful investors and short in firms purchased by previously unsuccessful investors earn abnormal returns of 5 basis points per day. These returns are not confined to small stocks nor to stocks in which the investors are likely to have inside information. Our results suggest that skillful individual investors exploit market inefficiencies to earn abnormal profits, above and beyond any profits available from well-known strategies based upon size, value, or momentum.
The paper is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=364000
Similar to Investor psychology and security market under and overreactions, Presentation Slides (20)
Are Investors Really Reluctant to Realize Their Losses? The Disposition Effec...David Hirshleifer
We examine how investor preferences and beliefs affect trading in relation to past gains and losses. The probability of selling as a function of profit is V-shaped; at short holding periods, investors are more likely to sell big losers than small ones. There is little evidence of an upward jump in selling at zero profits. These findings provide no clear indication that realization preference explains trading. Furthermore, the disposition effect is not driven by a simple direct preference for selling a stock by virtue of having a gain versus loss. Trading based on belief revisions can potentially explain these findings.
Paper available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1876594
Conversation, Observational Learning, and Informational CascadesDavid Hirshleifer
We offer a model in which sequences of individuals often converge upon poor decisions and are prone to fads, despite communication of the payoff outcomes from past choices. This reflects both direct and indirect action-based information externalities. In contrast with previous cascades literature, cascades here are spontaneously dislodged and in general have a probability less than one of lasting forever. Furthermore, the ability of individuals to communicate can reduce average decision accuracy and welfare.
Latest version of the paper is "Taking the Road Less Traveled by: Does Conversation Eradicate Pernicious Cascades?," Cao, H. Henry, Han, Bing and Hirshleifer, David A., http://ssrn.com/abstract=422180
Opportunism as a firm and managerial trait predicting insider trading profits...David Hirshleifer
We show that opportunistic insiders can be identified through the profitability of their trades prior to quarterly earnings announcements (QEAs), and that opportunistic trading is associated with various kinds of firm/managerial misconduct. A value-weighted trading strategy based on (not necessarily pre-QEA) trades of opportunistic insiders earns monthly 4-factor alphas of over 1% — much higher than in past insider trading literature and substantial/significant even on the short side. Firms with opportunistic insiders have higher levels of earnings management, restatements, SEC enforcement actions, shareholder litigation, and executive compensation. These findings suggest that opportunism is a domain-general trait.
See paper at https://ssrn.com/abstract=2635257
A theory of costly sequential bidding - Presentation SlidesDavid Hirshleifer
We propose a model of sequential bidding for a valuable object, such as a takeover target, when it is costly submit or revise a bid. An implication of the model is that bidding occurs in repeated jumps, a pattern that is consistent with certain types of natural auctions such as takeover contests. The jumps in bid communicate bidders' information rapidly, leading to contests that are completed with a small number of bids. The model provides several new results concerning revenue and efficiency relationships between different auctions, and provides an information-based interpretation of delays in bidding.
Prepublication version available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=161013
Previous empirical work on adverse consequences of CEO overconfidence raises the question of why firms hire overconfident managers. Theoretical research suggests a reason: overconfidence can benefit shareholders by increasing investment in risky projects. Using options- and press-based proxies for CEO overconfidence, we find that over the 1993–2003 period, firms with overconfident CEOs have greater return volatility, invest more in innovation, obtain more patents and patent citations, and achieve greater innovative success for given research and development expenditures. However, overconfident managers achieve greater innovation only in innovative industries. Our findings suggest that overconfidence helps CEOs exploit innovative growth opportunities.
Link to original paper https://ssrn.com/abstract=1598021.
We test how market overvaluation affects corporate innovation. Estimated stock overvaluation is very strongly associated with R&D, innovative output, and measures of innovative novelty, originality, and scope. R&D is much more sensitive than capital investment to overvaluation. Misvaluation affects R&D more via a non-equity channel than via equity issuance. We document how the sensitivity of R&D and innovative outcomes to misvaluation depends on growth, overvaluation, and turnover. The frequency of exceptionally high innovative inputs/outputs increases with overvaluation. This evidence suggests that market overvaluation may generate social value by increasing innovative output and by encouraging firms to engage in ‘moon shots.’ The paper is available at https://ssrn.com/abstract=2878418.
We find that innovative efficiency (IE), patents or citations scaled by research and development expenditures, is a strong positive predictor of future returns after controlling for firm characteristics and risk. The IE-return relation is associated with the loading on a mispricing factor, and the high Sharpe ratio of the Efficient Minus Inefficient (EMI) portfolio suggests that mispricing plays an important role. Further tests based upon attention and uncertainty proxies suggest that limited attention contributes to the effect. The high weight of the EMI portfolio return in the tangency portfolio suggests that IE captures incremental pricing effects relative to well-known factors.
Link to the paper: https://ssrn.com/abstract=1799675
We test whether and how equity overvaluation affects corporate financing decisions using an ex ante misvaluation measure that filters firm scale and growth prospects from market price. We find that equity issuance and total financing increase with equity overvaluation; but only among overvalued stocks; and that equity issuance is more sensitive than debt issuance to misvaluation. Consistent with managers catering to maintain overvaluation and with investment scale economy effects, the sensitivity of equity issuance and total financing to misvaluation is stronger among firms with potential growth opportunities (low book-to-market, high R&D, or small size) and high share turnover.
how to sell pi coins in South Korea profitably.DOT TECH
Yes. You can sell your pi network coins in South Korea or any other country, by finding a verified pi merchant
What is a verified pi merchant?
Since pi network is not launched yet on any exchange, the only way you can sell pi coins is by selling to a verified pi merchant, and this is because pi network is not launched yet on any exchange and no pre-sale or ico offerings Is done on pi.
Since there is no pre-sale, the only way exchanges can get pi is by buying from miners. So a pi merchant facilitates these transactions by acting as a bridge for both transactions.
How can i find a pi vendor/merchant?
Well for those who haven't traded with a pi merchant or who don't already have one. I will leave the telegram id of my personal pi merchant who i trade pi with.
Tele gram: @Pi_vendor_247
#pi #sell #nigeria #pinetwork #picoins #sellpi #Nigerian #tradepi #pinetworkcoins #sellmypi
Currently pi network is not tradable on binance or any other exchange because we are still in the enclosed mainnet.
Right now the only way to sell pi coins is by trading with a verified merchant.
What is a pi merchant?
A pi merchant is someone verified by pi network team and allowed to barter pi coins for goods and services.
Since pi network is not doing any pre-sale The only way exchanges like binance/huobi or crypto whales can get pi is by buying from miners. And a merchant stands in between the exchanges and the miners.
I will leave the telegram contact of my personal pi merchant. I and my friends has traded more than 6000pi coins successfully
Tele-gram
@Pi_vendor_247
when will pi network coin be available on crypto exchange.DOT TECH
There is no set date for when Pi coins will enter the market.
However, the developers are working hard to get them released as soon as possible.
Once they are available, users will be able to exchange other cryptocurrencies for Pi coins on designated exchanges.
But for now the only way to sell your pi coins is through verified pi vendor.
Here is the telegram contact of my personal pi vendor
@Pi_vendor_247
US Economic Outlook - Being Decided - M Capital Group August 2021.pdfpchutichetpong
The U.S. economy is continuing its impressive recovery from the COVID-19 pandemic and not slowing down despite re-occurring bumps. The U.S. savings rate reached its highest ever recorded level at 34% in April 2020 and Americans seem ready to spend. The sectors that had been hurt the most by the pandemic specifically reduced consumer spending, like retail, leisure, hospitality, and travel, are now experiencing massive growth in revenue and job openings.
Could this growth lead to a “Roaring Twenties”? As quickly as the U.S. economy contracted, experiencing a 9.1% drop in economic output relative to the business cycle in Q2 2020, the largest in recorded history, it has rebounded beyond expectations. This surprising growth seems to be fueled by the U.S. government’s aggressive fiscal and monetary policies, and an increase in consumer spending as mobility restrictions are lifted. Unemployment rates between June 2020 and June 2021 decreased by 5.2%, while the demand for labor is increasing, coupled with increasing wages to incentivize Americans to rejoin the labor force. Schools and businesses are expected to fully reopen soon. In parallel, vaccination rates across the country and the world continue to rise, with full vaccination rates of 50% and 14.8% respectively.
However, it is not completely smooth sailing from here. According to M Capital Group, the main risks that threaten the continued growth of the U.S. economy are inflation, unsettled trade relations, and another wave of Covid-19 mutations that could shut down the world again. Have we learned from the past year of COVID-19 and adapted our economy accordingly?
“In order for the U.S. economy to continue growing, whether there is another wave or not, the U.S. needs to focus on diversifying supply chains, supporting business investment, and maintaining consumer spending,” says Grace Feeley, a research analyst at M Capital Group.
While the economic indicators are positive, the risks are coming closer to manifesting and threatening such growth. The new variants spreading throughout the world, Delta, Lambda, and Gamma, are vaccine-resistant and muddy the predictions made about the economy and health of the country. These variants bring back the feeling of uncertainty that has wreaked havoc not only on the stock market but the mindset of people around the world. MCG provides unique insight on how to mitigate these risks to possibly ensure a bright economic future.
If you are looking for a pi coin investor. Then look no further because I have the right one he is a pi vendor (he buy and resell to whales in China). I met him on a crypto conference and ever since I and my friends have sold more than 10k pi coins to him And he bought all and still want more. I will drop his telegram handle below just send him a message.
@Pi_vendor_247
how to swap pi coins to foreign currency withdrawable.DOT TECH
As of my last update, Pi is still in the testing phase and is not tradable on any exchanges.
However, Pi Network has announced plans to launch its Testnet and Mainnet in the future, which may include listing Pi on exchanges.
The current method for selling pi coins involves exchanging them with a pi vendor who purchases pi coins for investment reasons.
If you want to sell your pi coins, reach out to a pi vendor and sell them to anyone looking to sell pi coins from any country around the globe.
Below is the contact information for my personal pi vendor.
Telegram: @Pi_vendor_247
The secret way to sell pi coins effortlessly.DOT TECH
Well as we all know pi isn't launched yet. But you can still sell your pi coins effortlessly because some whales in China are interested in holding massive pi coins. And they are willing to pay good money for it. If you are interested in selling I will leave a contact for you. Just telegram this number below. I sold about 3000 pi coins to him and he paid me immediately.
Telegram: @Pi_vendor_247
Lecture slide titled Fraud Risk Mitigation, Webinar Lecture Delivered at the Society for West African Internal Audit Practitioners (SWAIAP) on Wednesday, November 8, 2023.
BYD SWOT Analysis and In-Depth Insights 2024.pptxmikemetalprod
Indepth analysis of the BYD 2024
BYD (Build Your Dreams) is a Chinese automaker and battery manufacturer that has snowballed over the past two decades to become a significant player in electric vehicles and global clean energy technology.
This SWOT analysis examines BYD's strengths, weaknesses, opportunities, and threats as it competes in the fast-changing automotive and energy storage industries.
Founded in 1995 and headquartered in Shenzhen, BYD started as a battery company before expanding into automobiles in the early 2000s.
Initially manufacturing gasoline-powered vehicles, BYD focused on plug-in hybrid and fully electric vehicles, leveraging its expertise in battery technology.
Today, BYD is the world’s largest electric vehicle manufacturer, delivering over 1.2 million electric cars globally. The company also produces electric buses, trucks, forklifts, and rail transit.
On the energy side, BYD is a major supplier of rechargeable batteries for cell phones, laptops, electric vehicles, and energy storage systems.
Yes of course, you can easily start mining pi network coin today and sell to legit pi vendors in the United States.
Here the telegram contact of my personal vendor.
@Pi_vendor_247
#pi network #pi coins #legit #passive income
#US
What website can I sell pi coins securely.DOT TECH
Currently there are no website or exchange that allow buying or selling of pi coins..
But you can still easily sell pi coins, by reselling it to exchanges/crypto whales interested in holding thousands of pi coins before the mainnet launch.
Who is a pi merchant?
A pi merchant is someone who buys pi coins from miners and resell to these crypto whales and holders of pi..
This is because pi network is not doing any pre-sale. The only way exchanges can get pi is by buying from miners and pi merchants stands in between the miners and the exchanges.
How can I sell my pi coins?
Selling pi coins is really easy, but first you need to migrate to mainnet wallet before you can do that. I will leave the telegram contact of my personal pi merchant to trade with.
Tele-gram.
@Pi_vendor_247
how to sell pi coins in all Africa Countries.DOT TECH
Yes. You can sell your pi network for other cryptocurrencies like Bitcoin, usdt , Ethereum and other currencies And this is done easily with the help from a pi merchant.
What is a pi merchant ?
Since pi is not launched yet in any exchange. The only way you can sell right now is through merchants.
A verified Pi merchant is someone who buys pi network coins from miners and resell them to investors looking forward to hold massive quantities of pi coins before mainnet launch in 2026.
I will leave the telegram contact of my personal pi merchant to trade with.
@Pi_vendor_247
Investor psychology and security market under and overreactions, Presentation Slides
1. Electronic copy available at: https://ssrn.com/abstract=3181607
Investor Psychology and Security Market Under-
and Overreactions
Journal of Finance, December 1998.
Kent Daniel
David Hirshleifer
Avanidhar Subrahmanyam
Fin 9310 – Behavioral Finance Seminar
- April 4, 2018 -
2. Electronic copy available at: https://ssrn.com/abstract=3181607
Outline
1. Challenges to Behavioral Finance
2. This Paper’s Goals
3. Regularities in Anomalous Price Patterns
4. The Overconfidence Theory
• Psychological Evidence on Overconfidence and on Biased
Self-Attribution
• Model Setup
5. Model Implications
• Reaction to Public and Private Information
• Event Study Implications
• Price Scaled Variables
– Aggregate and Cross-Sectional Forecastability
• Short and Long Horizon Autocorrelations
• Short and Long Horizon Correlations with Information
Releases
• Corporate Finance Implications
6. Relation to Existing Theories
7. Conclusion
2
3. Challenges: Financial Market Anomalies
• There is now evidence of strong return predictability
• This evidence is largely responsible for the growth in interest
in behavioral finance.
• However, the over-reaction/under-reaction interpretations of-
ten given to this evidence appear contradictory:
[In the behavioral literature] ...[a]pparent anomalies
are viewed one at a time, and the same authors, ex-
amining different events, seem content with over-reaction
or under-reaction, and willing to infer that both war-
rant dropping market efficiency. (Fama 1998)
• The apparent inconsistency makes the behavioral stories ap-
pear no better than the market efficiency hypothesis:
The market efficiency hypothesis, of course, offers a
simple answer to this question – chance. Specifically,
the expected value of abnormal returns is zero, but
chance generates apparent anomalies that split ran-
domly between apparent over-reaction and apparent
under-reaction. (Fama 1998)
3
4. The Goal
• What do we need for a convincing behavioral theory?
• Fama (1998):
The alternative has a daunting task. It must spec-
ify what it is about investor psychology that causes
simultaneous under-reaction to some types of events
and over-reaction to others. The alternative must
also explain the range of observed results better
than the simple market efficiency story; that is,
[that] the expected value of abnormal returns is
zero, but chance generates deviations from zero
(anomalies) in both directions.
• Also, to answer Fama and others, we need to
– establish that there are consistent patterns in the data
– develop as-yet untested implications of the theory.
4
5. The Evidence Against Market Efficiency
Why not stick with efficient markets?
• These anomalies are sufficiently strong and regular that they
cause one to seriously question the efficient-markets paradigm:
– High Sharpe ratios are achievable with size, value, mo-
mentum and market timing strategies
(MacKinlay 1995, Campbell and Cochrane 1999).
– Apparent lack of correlation between returns of these strate-
gies and investors’ marginal utilities
(Lakonishok, Shleifer, and Vishny 1994).
– Out-of-sample (in time and location) have strongly es-
tablished these as regularities. (Davis, Fama, and French
2000, Fama and French 1998)
• Theories have analyzed why a small group of “optimizing,”
investors may not eliminate these patterns.
– But, we have not established what sort of behavioral bi-
ases might be at the root of these patterns
5
6. The Goals of This Paper
Based on this we have two goals in this paper:
• Catalog regularities among the empirical anomalies.
• Develop a behavioral model that:
– is based upon plausible micro-foundations.
– is parsimonious
– has implications consistent with the empirical regular-
ities.
– has as-yet untested empirical implications.
6
7. Regularities: Return Autocorrelations
• Return auto-correlations & covariances, empirically, look like:
1 2 33’
Favorable Private Signal
Unfavorable Private Signal
Price
Time
• Positive short-lag return autocorrelations (‘mo-
mentum’):
– Cross-Sectional: Jegadeesh and Titman (1993)
– Aggregate: Lo and MacKinlay (1988).
• Negative long-lag autocorrelations (long-run ‘over-
reaction’):
– Cross-Sectional: DeBondt and Thaler (1985, 1987), Chopra,
Lakonishok, and Ritter (1992)
– Aggregate: Fama and French (1988b), Poterba and Sum-
mers (1988); also Kim, Nelson, and Startz (1988), Daniel
(2001)
7
8. • implying an impulse-response function of the form:
1 2 33’
Favorable Private Signal
Unfavorable Private Signal
Full Information Value
Full Information Value
• This is consistent with the evidence presented in Jegadeesh
and Titman (2001).
8
9. Regularities: Underreaction to Public News
Public-event-date average security returns are typically of the
same sign as subsequent average long-run abnormal performance
(‘underreaction’)
• Seasoned Offerings: Loughran and Ritter (1995) and
Spiess and Affleck-Graves (1995); see, however, Brav and
Gompers (1997).
• Repurchase Announcements: Ikenberry, Lakonishok,
and Vermaelen (1995).
• Insider Trading: Seyhun (1986), Seyhun (1988). Rozeff
and Zaman (1988). Public trading strategy does not beat
bid-ask and transactions costs.
• Analysts’ Buy and Sell Recommendations: Wom-
ack (1996), Michaely and Womack (1999), Desai and Jain
(1995).
• Stock Splits: Grinblatt, Masulis, and Titman (1984),
Desai and Jain (1997).
• Dividend Initiations and Omissions: Michaely,
Thaler, and Womack (1995).
• Merger Announcements: Agrawal, Jaffe, and Mandelker
(1992), Agrawal, Jaffe, and Mandelker (1996), and Rau and
Vermaelen (1998).
• Venture Capital Distributions: Gompers and Lerner
(1998)
9
10. Regularities: Earnings/Return Correlations
• Short Horizon – Earnings surprises are positively correlated
with future returns
– Earnings Announcements: Bernard and Thomas
(1989, 1990), Brown and Pope (1996)
– Earnings Forecasts: Abarbanell and Bernard (1991,
1992), Mendenhall (1991).
• Long Horizon – High past growth negatively correlated with
future long horizon return.
– Past Accounting Growth Rates: Lakonishok, Shleifer,
and Vishny (1994)
10
11. Regularities: Price-Scaled Variables
Price-Scaled Variables are postively correlated with future re-
turns:
• Cross-Sectionally:
– E/P Ratio: Basu (1983), Jaffe, Keim, and Westerfield
(1989),
– B/P Ratio: Graham and Dodd (1934), Stattman (1980),
Rosenberg, Reid, and Lanstein (1985), DeBondt and Thaler
(1987), Fama and French (1992);
• Aggregate:
– D/P Ratio: Dow (1920), Ball (1978), Campbell and
Shiller (1988) and Fama and French (1988a)
– E/P Ratio: Fama and French (1988a)
– B/P Ratio: Kothari and Shanken (1997)
11
12. Behavioral Basis for the Model
• We retain expected utility theory; all investors are Bayesian
• However, informed investors
1. are overconfident about the value/precision of their pri-
vate information, and
2. update their estimate of the precision of their private in-
formation in a biased fashion (Self-Attribution Bias).
– Confidence rises more when trades are confirmed than
it falls when beliefs are disconfirmed.
• We make no assumptions about whether investors under- or
over-react, follow trends, etc.
– all of these effects are an implication of overconfi-
dence, rather than an assumption of the model
12
14. Evidence of Overconfidence
“...perhaps the most robust finding in the psychology of
judgement is that people are overconfident.”
—DeBondt/Thaler (1995)
Overconfidence of Professionals in their Judgments:
• Psychologists: Oskamp (1965)
• Physicians
& Nurses:
Christensen-Szalanski and Bushyhead
(1981), Baumann, Deber, and Thompson
(1991)
• Engineers: Kidd (1970)
• Attorneys: Wagenaar and Keren (1986)
• Negotiators: Neale and Bazerman (1990)
• Entrepeneurs: Cooper, Woo, and Dunkelberg (1988)
• Managers: Russo and Schoemaker (1992)
• Investment
Bankers:
Stael von Holstein (1972)
• Security Analysts
& Economic
Forecasters:
Ahlers and Lakonishok (1983), Elton,
Gruber, and Gultekin (1984), Froot and
Frankel (1989), DeBondt and Thaler
(1990), DeBondt (1991).
14
15. Evidence of Overconfidence
“...perhaps the most robust finding in the psychology of
judgment is that people are overconfident.”
-DeBondt/Thaler (1995)
• There is pervasive evidence of the overconfidence of profes-
sionals in their judgments
• People perceive themselves as:
– More able than they actually are.
– More able than average.
– More favorably than they are viewed by others.
• Individuals underestimate their prediction error variance in
experimental settings:
• In our model, investors are overconfident in their ability to
generate generate private information
– Gathering new private information through interviews with
firm management, etc.,
– Processing publicly available information
– Note that we do not specify how investors process infor-
mation, just that they are overconfident about the results
of this processing!
• In our model, informed agents’ overconfidence is manifested
in their overestimation of the precision of their information.
15
16. Self-Attribution Bias
• Individuals do not know their ability/precision; they must
estimate it.
• Behavioral evidence suggests that agents update estimates of
their precision in a biased fashion:
– People discount unfavorable information and magnify fa-
vorable information in updating beliefs about their own
abilities.
• Fischoff (1982), Langer and Roth (1975), Miller and Ross
(1975), Taylor and Brown (1988).
• Consistent with evidence of cognitive dissonance.
• In our model, their estimate of the precision (1/σ2
) of their
signals increases more with confirming information than it
decreases with dis-confirming information.
16
17. The Overconfidence Model
• “Idealized” setting is a continuum of risk-averse agents, some
of which receive information and are overconfident about it.
– For tractability, we have two continuous masses of agents:
1. Risk neutral overconfident informed traders (I’s)
2. Risk averse fully rational uninformed traders (U’s),
with exponential utility.
– This should yield the same qualitative results as the ide-
alized setting.
∗ In Daniel, Hirshleifer, and Subrahmanyam (2001), we
explore the ramifications of making both agents risk-
averse, and of having a full set of risky assets.
• Further, we assume that all informed receive the same signal.
s1 = θ +
where θ is the true firm value.
– The same qualitative results would obtain with a contin-
uum of ex-ante identical individuals who receive and are
overconfident about information signals of the form
s1,i = θ + + δi,
where δi is independent across risk averse individuals.
• We have two versions of the model
– a simplified static confidence version
– a full dynamic confidence version
17
18. The Static Overconfidence Model
Four dates:
0 1 2 3time
Private Signal Public Signal Realization
θ ∼ N(¯θ, σ2
θ) s1 = θ + s2 = θ + η. θ
1. Date 0: identical prior beliefs, equilibrium allocations.
2. Date 1: I’s receive a common noisy private signal about un-
derlying security value and trade with U’s.
• I’s underestimate σ2
(as σ2
C < σ2
)
3. Date 2: Noisy public signal arrives, re-trade.
4. Date 3: Conclusive public information arrives, liquidate, con-
sume.
• Because σ2
C is too small, stock prices:
– overreact to private information arrival
– underreact to public information arrival.
• Expected future price movement is proportional to −E[ ]
18
19. The Full (Dynamic) Confidence Model
time
Private Signal Public Signal Public Signal Public Signal
1 2 3 4
sI φ2 φ3 φ4
...
0
• Again, there is a single private signal at time 1
˜sI = ˜θ + ˜ ˜ ∼ N(0, σ2
)
• Now, at date 2 through T, a public signal ˜φt is released
˜φt = ˜θ + ˜ηt
where
– ˜ηt ∼ i.i.d. N(0, σ2
η).
– σ2
η is common knowledge.
• Φt is the average of all public signals through time t
Φt =
1
(t − 1)
t
τ=2
˜φτ
– Φt is a sufficient statistic for the set of all past φ’s
• The ad hoc variance updating rule I’s use is;
– If sign(sI − Φt−1) = sign(φt − Φt−1)
and |sI − Φt−1| < 2σΦ,t then vC,t = (1 + k)vC,t−1
– Otherwise vC,t = (1 − k)vC,t−1
• (1 + k)/(1 − k) is an index of the investor’s attribution bias
19
20. Equilibrium
• At each date, all individuals maximize expected utility as a
function of terminal wealth with respect to their beliefs.
• Prices are set such that the aggregate demands for the risky
and numeraire securities at each date equal aggregate supply.
• At each date individuals can trade at the market price to
modify their bundles of risky and numeraire securities.
• Individuals make decisions at each date based on their avail-
able information, including the market price, and I’s overcon-
fident beliefs about precision.
• It is common knowledge that I’s believe with certainty that
the noise variance of s1 is σ2
C and that U’s believe with cer-
tainty it is less than σ2
C.
20
21. Model Implications
• We determine the implications of the overconfidence model
for the documented equity-market anomalies:
1. Return Autocorrelations
2. Price-Scaled Variables
3. Event studies
– Selective and Non-selective events
4. Earnings/returns correlations at varying horizons
21
22. Model Implications: Return
Autocorrelations
• The response of prices to private information:
1 2 33’
Favorable Private Signal
Unfavorable Private Signal
Price
Time
• There are distinct Overreaction and Correction Phases.
• Overreaction occurs because of overconfidence in the initial
signal.
• Continuing overreaction occurs because arrival of public in-
formation on average causes the confidence of the informed
to grow.
– Thus, our theory suggests that the apparent short-term
underreaction suggested by momentum can be a result
of continuing overreaction.
∗ See Jegadeesh and Titman (2001).
– Price, on average, is pushed even further in the direction
of trader’s initial private information.
• In the correction phase price converges to the true value of
the security.
22
23. Price Movements with Static Confidence
• With just overconfidence, and no self-attribution bias, the
price response to a positive private signal is:
0 20 40 60 80 100 120
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
period
Av.Price
• This plot assumes a constant information arrival rate, expo-
nential decay.
• With this AR(1) price impulse response function , returns
are ARMA(1, 1), and autocorrelations are proportional to
−(1 − φ)φτ
, where φ is the rate of decay in the price plot
above.
• Negative autocorrelations at all lags and all horizons.
• Thus, static overconfidence is consistent with long-run rever-
sals, but not with short-run momentum.
23
24. Equilibrium of the Dynamic Model
• In equilibrium, the security price is:
˜Pt = EC[˜θ|sI, Φt].
– For tractability, we assume that at each t, I revalues the
security using Bayesian updating as if he knew the preci-
sion of his signal to be σC,t with certainty.
• Hard to solve analytically, so we simulate this.
• We perform 50,000 iterations, each time drawing ˜θ, ˜sI and
the set of φt’s from appropriate distributions.
• The simulation parameters for the results presented here are:
k = 0.75 k = 0.1
σ2
θ = 1 σ2
= 1
σ2
η = 7.5 T = 120
σ2
C,1 = 1
• Model not calibrated.
24
25. Simulation Results
• The (price) impulse response function for sI = 1
with Attribution Bias
without Attribution Bias
0 20 40 60 80 100 120
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
period
AveragePrice
• The price-change autocorrelogram for full simulation is:
10 20 30 40 50 60 70 80 90 100 110
0.02
0
0.02
0.04
0.06
0.08
0.1
lag
Autocorrelation
• This is consistent with the empirical evidence.
25
26. • The Long-Horizon Regression Coefficient for a regression of
price changes on price changes:
R(t, t + τ) = α + βR(t − τ, t) +
0 10 20 30 40 50 60 70 80 90 100
−1
−0.8
−0.6
−0.4
−0.2
0
0.2
0.4
horizon
correlation
26
27. Model Implications: Event Studies
• Basic implication of overconfidence model is (1) Overreaction
to Private Information, and (2) Underreaction to Public.
• The model does imply that a security’s return on a public-
information release date is negatively correlated with the fu-
ture return.
• However, counter-intuitively, the static confidence model does
not imply that the correlation of the future return with the
public signal is itself negative.
– Static Confidence Model ⇒ cov(P3 − P2, s2) = 0
• Intuition:
– Assume σθ = σ1 = σ2
Prior s1 s2
3 3
2 2
1 1
0 0 0
-1 -1
-2 -2
• Suppose s2 = 2, then E[θ|s2] = 1, S1 = 0 or 2 equally likely.
• Bias equally likely to be up or down.
• Even though underreaction, knowing s2 does not help predict
sign of future returns.
27
28. • If event is selective (based on mispricing or E[ |P1, s2]), it
will forecast future returns.
• Model is consistent with:
Dividend Initiations
Splits
SEO’s (IPO’s)
Repurchases
Div. Omissions
Event-Date
28
29. Implications: Selective Events
• Suppose managers act to correct pricing errors:
– issue shares when stock overvalued
– repurchase when stock undervalued
– signal (split, dividend increase, forecast earnings favor-
ably, boost accruals or cash flows) when undervalued
• However, market revalues stubbornly ⇒
– average abnormal post-event return has same sign as event-
date reaction.
• Consistent with any prior runup or rundown
• Informed outsider may also exploit misvaluations:
– Analyst buy/sell recommendations
– Toehold purchases, takeover bids?
• Implications:
– For good news events, the post-event abnormal re-
turns will be larger when B/M high or pre-event perfor-
mance poor (more negative initial valuation error).
∗ Ikenberry, Lakonishok, and Vermaelen (1995): B/M
and repurchase
– For bad news events, the post-event abnormal returns
more negative when B/M low or pre-event performance
good (more positive initial valuation error).
∗ Ikenberry, Rankine, and Stice (1996): prior perfor-
mance and splits
29
30. Implications: Earnings Growth and Future
Returns
• Many PEAD studies use non-selective measures – We need
full dynamic model to account for this reaction.
• In simulation, we define a public-information/“earnings” change
as:
∆et = ˜φt − Φt−1 = ˜φt − E[˜φt|φs, s = 2, . . . , t − 1]
• ∆et is therefore the deviation of φt from its expected value
based on all past public signals.
• Simulation: average correlation between information changes
and future prices changes looks like:
20 40 60 80 100 120
−3
−2
−1
0
1
2
3
4
5
6
7
x 10
−3
lag
Cross−correlation
• Broadly consistent with both post-earnings announcement
drift, and long-run post-earnings reversals.
– Magnitude of the effects depends on the intensity of the
attribution bias relative to the variance of the public sig-
nals.
30
31. Implications: Price Scaled Variables
• High price-scaled measures of fundamentals (e.g., E/P, D/P,
B/M, 1/M) imply high future returns.
• Favorable private signal ⇒ M ↑, B/M ↓.
– Overreaction ⇒ long run M ↓
• Adverse private signal ⇒ M ↓, B/M ↑.
– Overreaction ⇒ long run M ↑
• Therefore, high B/M ⇒ high long run future returns.
• Aggregate price-scaled measures will forecast future market
returns
– if overconfidence about economy-wide information.
• Price-scaled measures will forecast cross sectional patterns
– if overconfidence about firm-specific information.
• Price-scaled variables can dominate standard risk-measures if
investors are sufficiently overconfident.
• Standard fundamental ratio anomalies support overreaction
theories, refute underreaction theories.
31
32. Implications: Managerial Actions
• Raise capital when fundamental ratios are low (stock overval-
ued)
• Issue shares after firm’s stock price has recently increased
(consistent with Lucas and McDonald (1990).)
• If overconfidence about common factors, issue after firm’s in-
dustry or stock market has risen or B/M low
(consistent with IPO evidence of Pagano, Panetta, and Zin-
gales (1998)).
• When book/market low, favor public over rights issues.
• When book/market low, favor equity over debt issues.
• When book/market low or after firm’s stock has risen, favor
repurchase over dividend.
Should managers disclose as much as possible prior to issuance?
• Can exploit overconfidence by not disclosing.
• But sometimes public disclosure can intensify an overreaction.
32
33. Relation to Existing Theories
Overconfidence:
• Kyle and Wang (1997): being overconfident as a commitment
to trade aggressively.
• Wang (1998): Overconfidence → info-based trading without
noise traders.
• Odean (1998): Overconfidence with private but not a public
signal, determinants of volatility, volume, and market depth.
Overconfidence increases volatility, overreaction to private
signals,
• Caball´e and S´akovics (1996): Beliefs about beliefs about...
• Benos (1996): Kyle (1985) model with overconfidence.
Positive Feedback Strategies
• DeLong, Shleifer, Summers, and Waldmann (1990): Security
autocorrelation patterns based on mechanistic positive feed-
back investment strategies.
Berk (1995): Recognizes that market value reflects discounting of
risk.
33
34. Noise Trader Approach
• Prices are moved by trading that seems unrelated to the ar-
rival of information.
• Also important: investor mistakes involving misinterpretation
of genuine new information.
• Noise traders can be viewed as overconfident traders taking
the limit, holding constant the individual’s perceived preci-
sion, as his signal becomes very noisy.
Do overconfident traders go broke?
• Not if overconfidence helps them intimidate others, or encour-
ages high-return risks.
34
35. Further Empirical Implications
• Firms should prefer debt over equity when B/M is high or
following stock-price rundown.
• Firms should prefer repurchases over dividends when B/M is
low.
• Positive correlation between initial event reaction and post-
event performance.
• Less under-reaction to non-selective corporate events.
• Positive abnormal returns after toehold disclosures
• Post-event performance better for good-news events when
B/M high or pre-event performance poor.
• Post-event performance worse for bad news events when B/M
low or pre-event performance good.
35
36. Other Comments on Overconfidence
The over-weening conceit which the greater part of men have of
their own abilities, is an ancient evil remarked by the philosophers
and moralists of all ages.
...
The distant prospect of hazards, from which we can hope to ex-
tricate ourselves by courage and address, is not disagreeable to
us, and does not raise the wages of labour in any employment.
—The Wealth of Nations, p. 107, 110
• Smith: overconconfidence affects market prices in enterprises
where outcome depends on ability.
• Does the ‘overweening conceit’ of mankind affect stock market
prices?
36
37. Conclusions
• Approach to anomalies:
– Parsimonious
– Based on strong psychological evidence
– Explains wide range of phenomena plus new evidence
• Possible further avenues:
– Empirical testing
– Institutions versus individuals
37
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