Barry Ritholtz is a Bloomberg View columnist. He founded Ritholtz Wealth Management and was chief executive and director of equity research at FusionIQ, a quantitative research firm. He blogs at the Big Picture and is the author of Bailout Nation: How Greed and Easy Money Corrupted Wall Street and Shook the World Economy.

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Human Behavior and Startups - BloombergQuint

This trash was spread around a driveway on Galveston Street in the early morning hours, Aug. 9. A few other homes in the neighborhood also had their trash disturbed recently. Photo/Angela Denning

But the bears are just doing what comes natural, according to Rich Lowell, a biologist with the Alaska Department of Fish and Game. Bears naturally search for food and with over a thousand trash bins in town people are giving bears a lot of free meals.

KFSK reporter, Angela Denning, sat down with Lowell to talk about the bears. He says its human behavior that needs to change.

The local ADF&G office along with the Petersburg Police Department are running a live bear trap in town to try and relocate some bears. Lowell says if you see the trap in your neighborhood to keep pets and children away from it because the door is spring loaded and potentially dangerous. But, he adds, that it wont matter if bears are relocated if residents dont change their behavior. There will be more bears that come along and do the same thing.

This bear was sighted in Seversons Subdivision this month. Photo/Shauna Pitta-Rosse.

For information on electric fences for bears, check out this ADF&G webpage.

For more information about coexisting with bears, visit http://www.alaskabears.alaska.gov.

Link:
Bears in Petersburg trash a human problem to solve - KFSK

This post was written with Chris Kerlow and Craig Basinger.

The essence of money management can be encapsulated in searching for investments that are trading below their intrinsic value.

In theory, it works like this: Buying these companies low, and as the market comes around to realize their intrinsic value, the price moves higher and we sell for profits.

This is the activity of price discovery and makes for a healthy market as participants buy and sell in the attempt to profit from the return to intrinsic value, see chart. But there is a new big player in the market that doesnt care about price discovery, the Exchange Traded Fund (ETF) or other passive index investment strategies. A passive ETF is price agnostic. They need to buy the shares of the underlying basket of stocks that comprise the index, regardless of the price of those stocks. Their mandate is not to buy low, sell high; it is to buy quickly, minimizing tracking error, giving investors the exact exposure they are looking for. Most indices are based on market weight, and do not discriminate for liquidity.

With ETFs swelling in assets and active managers shrinking over the past years, the number of participants embarking upon the admirable task of price discovery is lower. All else equal, this suggests it will likely take longer for the true intrinsic value to be found. Meaning that the Warren Buffet style investors will need to wait longer for the tide to go out and show who is swimming naked, as the oracle from Omaha eloquently once said. This could be one of the reasons why active management has lagged for the past several years, as the bull market marches higher and passive funds pile more and more capital into the fastest growing stocks, with no regard to price or value.

Active managers typically classify their style into specific architypes. Value investors buy beaten down companies that are believed to have a larger gap between the current price and intrinsic value. Growth managers buy at higher levels, thinking that the market is underestimating the growth prospects. Top down managers make macro bets, focusing on sector allocation and picking the best solution to portray their view. Or, a manager might focus on specific factors like large cap, dividend companies which narrows their focus and inherently investment outcomes.

All things considered, most managers use a varying combination of different approaches. All are buying companies they believe to be below intrinsic value and selling those at or above intrinsic value. But with a weaker price discovery mechanism in the market, perhaps it is time for active managers to change their approach. We believe active managers can add the most value when the market is inefficient see chart below. And inefficiencies are often caused by human behavior.

Where do these inefficiencies stem from? The innate human behaviors that have helped us survive and work our way to the top of the food chain also come with flaws that are exhibited in investing. We are accumulating a variety of research on these biases and looking for investing opportunities that these repeating tendencies present. We will highlight several of these behavioral biases to help our readers try to avoid them in their own portfolios and potentially profit from taking advantage of others irrational behaviors.

Overreaction

Markets are largely efficient, some more than others, but it is undeniable that inefficiencies are present. Some inefficiencies take time to rectify themselves and some come and go quickly. These aberrations are highlighted by the markets reaction to earnings and brief periods of extreme volatility (flash crashes).

As an example, during the May 2010 flash crash, we saw the U.S. market lose a trillion dollars of market cap in 36 minutes, only to rebound quickly thereafter. It would be interesting to hear Eugene Fama explain how that is a characteristic of an efficient market. Regulations were put in place to avoid such an event from occurring again (LULD) but they proved to be inadequate. On August 24, 2015 we experienced another flash crash with many ETFs becoming unhinged from their implied value. The limit up, limit down (LULD) regulations put in place during the previous flash crash exacerbated the problem as market markers of ETFs were uncertain of the true value of the basket that investors were trying to buy / sell, so the market makers vanished.

We vividly remember sitting at our desks that Monday morning as the Dow fell 1,000 in just minutes. What caught our attention more than the massive fall in stock prices, was the dislocation between sector ETFs and the basket of stocks they were built to follow. IAK, the iShares insurance ETF was down 42% in the first 12 minutes of trading, while at the same time the largest underlying constituents like AIG and MetLife were down only 10%. The market makers had seemingly disappeared. Everything did come back to reality just a short while later as the ETF rallied 68% off the lows, as shown on the chart below.

Although this exact scenario may not happen again, over reaction to new information will. This stems from the availability heuristic where investors focus on the most recent information often ignoring the long term picture. Our Market Ethos on Earnings Overreaction, goes into detail on how high quality companies that see a share price slammed on a negative earnings report tend to make back the share price loss quickly, while low quality companies that gap up on earnings tend to give back the gains.

Confirmation Bias and Herding

Two weeks ago we wrote about the flaws in investing with the herd. Our innate tendency to follow the herd has been ingrained in us through Darwinism and is glaringly obvious in investing, but often ends poorly, especially for those late to the party. The next chart exemplifies what we are referring to. You can see when the herd, in this case speculators on the Canadian dollar, gets to extreme levels, it has been the exact time the momentum shifts in the other direction. Adding to this problem is a confirmation bias, when investors place more value on information that corroborates with their own opinion. The flaws are simply human nature and unlikely to go away anytime soon.

Understanding that, if you are able to get in early before the herd follows, you should be able to make some nice profits. To do so, you need to take a contrarian view, which conflicts with the comforting feelings you get from going with the herd. Back in April, we published a piece that outlined a strategy to take advantage of these biases. Best to be Unloved goes into greater detail but the idea is simple: companies that are largely unloved by the analyst community, but then start to get upgraded, have seen outsized gains versus companies that have mainly buy ratings, last chart. The premise is rather simple, these unloved companies could have depressed valuations because they do not have the herd supporting the stock. Then as analysts start to recognize a change in business fundamentals, they upgrade the stock. Often other analysts follow their peers, subsequently upgrading their rating of the stock. More upgrades tend to follow, giving investors confirmation and feeding the herding behaviour.

Conclusion

These are just a couple of examples of how our hardwired heuristics and bias show up in investment decisions. We are engulfed in the subject matter as we continue to research behavioral finance to help avoid pitfalls in our own investment process as well as find ways of exploiting these repeated flaws exhibited in the broad market. We look forward to sharing the findings with you, our readers, to help you avoid making behavioral mistakes.

Charts are sourced to Bloomberg unless otherwise noted.

Twitter:@sobata416 @ConnectedWealth

Any opinions expressed herein are solely those of the authors, and do not in any way represent the views or opinions of any other person or entity.

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Uncovering Value: Price Discovery And Irrational Investing Behavior - See It Market (blog)

Alice H. Eagly, Northwestern University

(The Conversation is an independent and nonprofit source of news, analysis and commentary from academic experts.)

Alice H. Eagly, Northwestern University

(THE CONVERSATION) Its no secret that Silicon Valley employs many more men than womenin tech jobs. Whats much harder to agree on is why.

The recent anti-diversity memo by a now former Google engineer has pushed this topic into the spotlight. The writer argued there are ways to explain the gender gap in tech that dont rely on bias and discrimination specifically, biological sex differences. Setting aside how this assertion would affect questions about how to move toward greater equity in tech fields, how well does his wrap-up represent what researchers know about the science of sex and gender?

As a social scientist whos been conducting psychological research about sex and gender for almost 50 years, I agree that biological differences between the sexes likely are part of the reason we see fewer women than men in the ranks of Silicon Valleys tech workers. But the road between biology and employment is long and bumpy, and any causal connection does not rule out the relevance of nonbiological causes. Heres what the research actually says.

There is no direct causal evidence that biology causes the lack of women in tech jobs. But many, if not most, psychologists do give credence to the general idea that prenatal and early postnatal exposure to hormones such as testosterone and other androgens affect human psychology. In humans, testosterone is ordinarily elevated in males from about weeks eight to 24 of gestation and also during early postnatal development.

Ethical restraints obviously preclude experimenting on human fetuses and babies to understand the effects of this greater exposure of males to testosterone. Instead, researchers have studied individuals exposed to hormonal environments that are abnormal because of unusual genetic conditions or hormonally active drugs prescribed to pregnant women. Such studies have suggested that early androgen exposure does have masculinizing effects on girls juvenile play preferences and behavior, aggression, sexual orientation and gender identity and possibly on spatial ability and responsiveness to cues that certain behaviors are culturally female-appropriate.

Early hormonal exposure is only one part of a complex of biological processes that contribute to sexual differentiation. Driven by both direct and roundabout messages from the X and Y chromosomes, the effects of these processes on human psychology are largely unknown, given the early stage of the relevant science.

Other studies inform the nature-nurture question by comparing the behaviors of boys and girls who are so young that socialization has not exerted its full influence.

Early sex differences emerge mainly on broad dimensions of temperament. One such dimension is what psychologists call surgency; its greater in boys and manifests in motor activity, impulsivity and experiencing pleasure from high-intensity activities. The other dimension is in what we term effortful control; its greater in girls and emerges in the self-regulatory skills of greater attention span, ability to focus and shift attention and inhibitory control. This aspect of temperament also includes greater perceptual sensitivity and experience of pleasure from low-intensity activities.

This research on temperament does suggest that nature instills some psychological sex differences. But scientists dont fully understand the pathways from these aspects of child temperament to adult personality and abilities.

Another approach to the women-in-tech question involves comparing the sexes on traits thought most relevant to participation in tech. In this case, it doesnt matter whether these traits follow from nature or nurture. The usual suspects include mathematical and spatial abilities.

The sex difference in average mathematical ability that once favored males has disappeared in the general U.S. population. There is also a decline in the preponderance of males among the very top scorers on demanding math tests. Yet, males tend to score higher on most tests of spatial abilities, especially tests of mentally rotating three-dimensional objects, and these skills appear to be helpful in STEM fields.

Of course people choose occupations based on their interests as well as their abilities. So the robust and large sex difference on measures of people-oriented versus thing-oriented interests deserves consideration.

Research shows that, in general, women are more interested in people compared with men, who are more interested in things. To the extent that tech occupations are concerned more with things than people, men would on average be more attracted to them. For example, positions such as computer systems engineer and network and database architect require extensive knowledge of electronics, mathematics, engineering principles and telecommunication systems. Success in such work is not as dependent on qualities such as social sensitivity and emotional intelligence as are positions in, for instance, early childhood education and retail sales.

Women and men also differ in their life goals, with women placing a higher priority than men on working with and helping people. Jobs in STEM are in general not viewed as providing much opportunity to satisfy these life goals. But technology does offer specializations that prioritize social and community goals (such as designing healthcare systems) or reward social skills (for instance, optimizing the interaction of people with machines and information). Such positions may, on average, be relatively appealing to women. More generally, womens overall superiority on readingand writing as well as social skillswould advantage them in many occupations.

Virtually all sex differences consist of overlapping distributions of women and men. For example, despite the quite large sex difference in average height, some women are taller than most men and some men are shorter than most women. Although psychological sex differences are statistically smaller than this height difference, some of the differences most relevant to tech are substantial, particularly interest in people versus things and spatial ability in mental rotations.

Given the absence of clear-cut evidence that tech-relevant abilities and interests flow mainly from biology, theres plenty of room to consider socialization and gender stereotyping.

Because humans are born undeveloped, parents and others provide extensive socialization, generally intended to promote personality traits and skills they think will help offspring in their future adult roles. To the extent that women and men have different adult lives, caregivers tend to promote sex-typical activities and interests in children dolls for girls, toy trucks for boys. Conventional socialization can set children on the route to conventional career choices.

Even very young children form gender stereotypes as they observe women and men enacting their societys division of labor. They automatically learn about gender from what they see adults doing in the home and at work. Eventually, to explain the differences they see in what men and women do and how they do it, children draw the conclusion that the sexes to some extent have different underlying traits. Divided labor thus conveys the message that males and females have different attributes.

These gender stereotypes usually include beliefs that women excel in qualities such as warmth and concern for others, which psychologists label as communal. Stereotypes also suggest men have higher levels of qualities such as assertiveness and dominance, which psychologists label as agentic. These stereotypes are shared in cultures and shape individuals gender identities as well as societal norms about appropriate female and male behaviors.

Gender stereotypes set the stage for prejudice and discrimination directed toward those who deviate from gender norms. If, for example, people accept the stereotype that women are warm and emotional but not tough and rational, gatekeepers may close out women from many engineering and tech jobs, even those women who are atypical of their sex. In addition, women talented in tech may falter if they themselves internalize societal stereotypes about womens inferiority in tech-relevant attributes. Also, womens anxiety that they may confirm these negative stereotypes can lower their actual performance.

Its therefore not surprising that research provides evidence that women generally have to meet a higher standard to attain jobs and recognition in fields that are culturally masculine and dominated by men. However, there is some recent evidence of preferential hiring of women in STEM at U.S. research-intensive institutions. Qualified women who apply for such positions have a better chance of being interviewed and receiving offers than do male job candidates. Experimental simulation of hiring of STEM faculty yielded similar findings.

Many pundits make the mistake of assuming that scientific evidence favoring sociocultural causes for the dearth of women in tech invalidates biological causes, or vice versa. These assumptions are far too simplistic because most complex human behaviors reflect some mix of nature and nurture.

And the discourse is further compromised as the debate becomesmore politicized. Arguing for sociocultural causes seems the more progressive and politically correct stance today. Arguing for biological causes seems the more conservative and reactionary position. Fighting ideological wars distracts from figuring out what changes in organizational practices and cultures would foster the inclusion of women in tech and in the scientific workforce in general.

Politicizing such debates threatens scientific progress and doesnt help unravel what a fair and diverse organization is and how to create one. Unfortunately, well-meaning efforts of organizations to promote diversity and inclusion can be ineffective, often because they are too coercive and restrictive of managers autonomy. The outrage in James Damores manifesto suggests that Google might want to take a close look at its diversity initiatives.

At any rate, neither nature-oriented nor nurture-oriented science can fully account for the underrepresentation of women in tech jobs. A coherent and open-minded stance acknowledges the possibility of both biological and social influences on career interests and competencies.

Regardless of whether nature or nurture is more powerful for explaining the lack of women in tech careers, people should guard against acting on the assumption of a gender binary. It makes more sense to treat individuals of both sexes as located somewhere on a continuum of masculine and feminine interests and abilities. Treating people as individuals rather than merely stereotyping them as male or female is difficult, given how quickly our automatic stereotypes kick in. But working toward this goal would foster equity and diversity in tech and other sectors of the economy.

This article was originally published on The Conversation. Read the original article here: http://theconversation.com/does-biology-explain-why-men-outnumber-women-in-tech-82479.

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Does biology explain why men outnumber women in tech? - San Francisco Chronicle