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Alpha Theory Blog - News and Insights

92 posts categorized "Portfolio Strategy"

November 10, 2017

Predictably Insightful: Recap of the Behavioral Alpha Conference

 

This is a picture of me and Dan Ariely, author of “Predictably Irrational” and five other great books on decision pitfalls we all fall into. Dan was the keynote speaker at Behavioral Alpha 2017 an event put on by our friends at Essentia Analytics and we were proud to help sponsor.

 

Behavioral Alpha

 

The day was packed with great speakers including:

- Dan Ariely: “Behavioral Finance in Practice” 

- Denise Shull talking about “Your Senses, Feelings & Emotions are the Ultimate Dataset”

- Clare Flynn Levy: "Applying Behavioral Finance to Your Own Investment Process" 

- Fireside Chat with Mark Baumgartner: “Why Asset Allocators Care About Behavioral Analysis” 

- Cameron Hight: “Mistakes Managers Make & How to Fix Them”

- Peer Idea Exchange: Paul Sonkin and Paul Johnson: “Pitching the Perfect Investment:

- Managing the Tensions Between Analysts and Managers” 

- Dave Winsborough: “How the Collective Personality of Your Team Affects Performance”

 

Here’s a quick recap of some of the takeaways:

Dave Winsborough discussed ways that we can build better teams by understanding the goal we’re trying to accomplish, the needed components to accomplish that goal, and measuring the team participants to make sure that the team has all of the necessary components. It’s a relatively straightforward idea that should be applicable to almost any team.

Denise Shull discussed ways we can become better in tune with our feelings and emotions with the idea of learning when and how to leverage those feelings. Learning how to identify our own emotions is a powerful first step towards being able to mute the negative emotions and take advantage of the positive (signals).

Much of the conference was on emotion and bias and how they cause us to make poor decisions. I completely agree, but that’s not my expertise. I spent much of my time talking the processes that help mitigate bias. This primarily involved making our assumptions and decision process explicit so that they can be judged and analyzed.

Dan Ariely gave several fascinating anecdotes like how casinos are the best at applying behavioral tools, how company internal satisfaction surveys have predictive power for stock performance, how Intuit is giving teams time and money to try bold new initiatives to help them get over the risk of projects that fail, a weight scale that doesn’t show your weight (but tracks it over time) is a much better way to lose weight than one that gives immediate feedback that is subject to good-habit-breaking volatility, and how people in the next to the lowest tax bracket are the ones most opposed to minimum wage hikes because it could push them into the lowest rung of society. His major takeaway was the bias and personality are tough to eliminate so you have to create habits, rules, and routinized behaviors that help us do the things we say we want to do (very Alpha Theory😉).

Clare Flynn-Levy showed how investors can make better decisions by capturing some basic information about themselves and their decisions. Taking the time to tie those data points together can help us better understand when we make good decisions and when we make poor decisions. By understanding these cues when they’re happening we can take advantage of the positive and avoid the negative.

Mark Baumgartner discussed his time at the Ford Foundation and Institute of Advanced Studies and some of the things he’s seen in the managers he evaluates. He said that about 10% of the managers he meets have some form of structured process around behavioral science, decision making, portfolio management, position sizing, etc. He believes that the primary value of a manager isn’t based on these processes, but he believes there is a lot of easy to pick up alpha form implementing process. He would like to see his managers embrace it more actively but says the industry moves glacially while the products that help improve the process are evolving very fast.

The room was full of managers and allocators. There was a self-selection bias, but the crowd truly embraced the concepts for how to be better using the behavioral science discussed during the day. In fact, the crowd asked amazing questions and one of my favorite parts of the day was from a member of the audience that was expanding on his thoughts about the difficulties of capturing alpha. He said the number of investors has increased from 5,000 to 1 million over 50 years. How do you reverse that trend when it is one of the highest paid professions, where you get to work with amazing people, research a broad range of interest, get to meet leaders in industry, academics, and government, and be exposed to an array of amazing ideas? If I’m ambitious and at the top of my class, why would I not pursue that profession.

Hmmmmm, maybe we can ask Dan Ariely if he has some creative way to change that behavior.

 

October 06, 2017

Poker: Art vs Science

 

“People describe poker as a game of art and science. Both intuition and science have merit, but the best players approach the game very quantitatively.” – Liv Boeree, Professional Poker Player

Our COO, Graham Stevens, and I met over a poker table. We’ve been playing together for many years and he was recently watching an Oxford Lecture Series video by Liv Boeree that he turned me on to.

Liv is a very successful poker player with a physics degree from the University of Manchester. She was discussing the use of Game Theory Optimal (GTO) play and the use of GTO Tables to break decisions down into ranges of hands based on different situations to aid poker players in knowing the optimal decision (Bet (big or small), Check, Fold).

She stated that the best players in the world all employ GTO. And even though all players assume their opponents are playing GTO, it is incredibly difficult to exploit those predicted decisions because they are optimal. In an interesting exchange in the video, Igor Kurganov, another very successful poker player who was in the audience, said that intuition (playing the player instead of playing the cards) factors into his decisions, but only to a small degree. He said that the best intuition can do is change a 50/50 bet to a 55/45.

The parallels to investing and Alpha Theory are clear. At Alpha Theory, we allow firms to build their own Game Theory Optimal system to figure out the “optimal” amount to bet on each position in their portfolio. And we find that firms that use intuition instead of their model lose to the hypothetical model performance about 75% of the time.

The reasons portfolio managers choose to vary from their model are numerous, but have a common theme; there is an intuition that the model isn’t capturing. Granted investing is not poker. Poker has a finite set of variables and permutations comparted to the seemingly infinite number of variables to consider in investing. But even still, just like in poker, the world-class players are going to be the ones that are following the model and only making small tweaks for intuition.

**Do I practice what I preach? A note on my own poker play. I do not play GTO because I have not memorized the tables. I know some of the shortcut rules for when to bet and fold pre-flop and I can do a rough calculation of pot odds post-flop but that’s the extent of my skills. If my buddies would let me pull out my computer while I’m sitting at the table, I would follow GTO.  If I were playing for a living, I would learn and follow the model.

 

September 14, 2017

Asset Manager Reliance on Human Judgement vs Machine

Asset management is the industry most reliant on human judgement according to a recent Price Waterhouse study on Data Analytics.

 

Screen Shot 2017-09-14 at 10.14.59 AM

 

Asset managers rely on human judgement 3x more than the next industry. For an industry with some of the best and brightest, we seem to be far behind. There is no expectation that this will happen overnight, but at a bare minimum we need to be experimenting with ways to enhance our judgement with machines.

Alpha Theory has been doing just that for over 10 years and our clients have outperformed the average hedge fund by over 2x. Getting started is not hard. Adopting “machine” does not require a wholesale change as all of our clients operate with Man + Machine. What it does require is an acceptance that Man alone is generally inferior to Man + Machine and a cultural embrace of the “machine” as an enhancement to the daily judgements we all make.

The reliance on human judgement will fall over time for asset managers. Do not be the last the change.

 

 

August 18, 2017

Man Versus Model of Man: Lewis Goldberg

I recently read an article by Jason Zweig and saw a reference to Lewis Goldberg’s, “Man Versus Model of Man” paper on Expert Studies in the 1970 Psychological Bulletin. There are hundreds of published studies that have a similar theme. Give an expert any and all available data that they want and ask them to make a judgement germane to their field of expertise (examples include Oncologist – how long will a patient live, Parole Board – who is most likely to recidivate, Wine Expert – price of wine at auction, etc.)

The experts tell the scientist which variables are most important in their decision and the scientist goes off and builds a model and compares the model’s results to the forecasts of the “experts.” Over the past 60 years, hundreds of expert studies have been performed and show that the model beats or ties the expert 94% of the time (1).

There was one of Goldberg’s quote about the use of models versus clinical decision making made me laugh:

Such an enterprise, originally viewed with considerable disdain by clinical psychologists, has recently weathered a period of intense controversy (Gough, 1962; Meehl, 1954; Sawyer, 1966), and may soon become a reasonably well accepted procedure in psychology—if not in medicine, stock forecasting, and other professional endeavors.

Consequently, it now seems safe to assert rather dogmatically that when acceptable criterion information is available, the proper role of the human in the decision-making process is that of a scientist: (a) discovering or identifying new cues which will improve predictive accuracy, and (b) constructing new sorts of systematic procedures for combining predictors in increasingly more optimal ways.

This quote was written 46 years ago yet clinical judgement still dominates psychology, medicine, and stock forecasting. Given the evidence, it is hard to argue against model-based decision making or man + model, but expert judgement still dominates.

The experts that will dominate the future (and are already beginning to do so) are the ones that embrace models as an extension of their own expertise. Models do not replacement human judgement. The parameters models are built upon are determined by experts. Experts also are required to intuit when exceptions to the model are necessary.

My belief is that Lewis Goldberg’s prediction will come true in the next decade as computing power, statistical techniques, software, and zeitgeist have grown to a point where Man + Machine will become the rule instead of the exception.

Here’s a few other great quotes from Lewis Goldberg’s article:

- Mathematical representations of such clinical judges can often be constructed to capture critical aspects of their judgmental strategies.

- The results of these analyses indicate that for this diagnostic task models of the men are generally more valid than the men themselves. Moreover, the finding occurred even when the models were constructed on a small set of cases, and then man and model competed on a completely new set.

- Ten years of research on the clinical judgment process have demonstrated that for many types of common clinical decisions and for many sorts of clinical judges, a simple linear regression equation can be constructed which will predict the responses of a judge at approximately the level of his own reliability. For documentation of this assertion and for details of the methodology, see Hoffman (1960), Hammond, Hursch, and Todd (1964), Naylor and Wherry (1965), and Goldberg (1968). While such regression models have 424 LEWIS R. GOLDBERG been utilized (probably somewhat inappropriately) to explain the manner in which clinicians combine cues in making their diagnostic and prognostic decisions (see Green, 1968; Hoffman, 1968), there is little controversy about their power as predictors of the clinical judgments

 

(1) “Comparative Efficiency of Informal and Formal Prediction Procedures” – William Grove and Paul Meehl, published in Psychology, Public Policy, and Law (1996)

July 28, 2017

Crist on Value

There is a paper, famous in value investing circles, called Crist on Value. It is a chapter from a book written by horse handicapper Steven Crist who opines on the short-comings of the average horse bettor. Its popularity amongst value investors is due to its sage advice that can readily be applied to investing. The article is of moderate length and a must read for any fundamental investor. I’ve taken the liberty of highlighting a few of quotes pertinent to our profession:

- “How often have you or a fellow track-goer opined that you're a pretty good handicapper but you really need to work on your betting strategies or your so-called money management? The problem with this line of thinking is that it suggests betting is some small component of the game, which is like pretending that putting is a minor part of championship golf.” INVESTOR CORROLARY: Investors that believe they are good stock pickers but just don’t get the position size right.

- “Even a horse with a very high likelihood of winning can be either a very good or a very bad bet, and the difference between the two is determined by only one thing: the odds. A horseplayer cannot remind himself of this simple truth too often, and it can be reduced to the following equation: Value = Probability x Price." INVESTOR CORROLARY: Investments decision process requires three components: profit from win, cost from loss, and probabilities of each.

- “Now ask yourself honestly: Do you really think this way when you're handicapping (in probability-weighted returns)? Or do you find horses you "like" and hope for the best on price? Most honest players will admit they follow the latter path. This is the way we all have been conditioned to think: Find the winner, then bet. Know your horses and the money will take care of itself.” INVESTOR CORROLARY: Every investment requires story and value. With both, you don’t have an investment.

- “Sticking to your guns is easier said than done, but it is the only way to win in the long run. The horseplayer who wants to show a profit must adopt a cold-blooded and unsentimental approach to the game that is at variance with both the "sporting" impulse to be loyal to your favorite horses and the egotistical impulse to stick with your initial selection at any price. This approach requires the confidence and Zen-like temperament to endure watching victories at unacceptably low prices by such horses.” INVESTOR CORROLARY: If you’re human, you’re subject to bias and emotion. Define rules and procedures in advance that highlight discrepancies between your actions and your rules.

- “I cannot argue in good conscience that Two Item Limit had precisely a 60 percent chance of victory as opposed to 57 or 63 percent, and I doubt that such calibration is in fact achievable. It is, however, possible through experience to get close enough that if you demand sufficient value to cover the margin of error, you should outperform the competition-your fellow horseplayers.” INVESTOR CORROLARY: Coming up with probabilities for stock price outcomes are even more subjective, but that doesn’t mean you can skip the exercise. Play around with a range of outcomes and figure out what is “too conservative” and “too aggressive” to give you comfort for where the probability should be.

 - “If every horseplayer but you were a certifiable idiot, betting at random on names and colors, you would win every day. Conversely, if the only people betting into the pool were the small number of professionals who make a living this way, your chances for long-term victory would be slim.” INVESTOR CORROLARY: How does your competition in the stock market stack up today?

- “Your opportunity for profit at the racetrack consists entirely of mistakes that your competition makes in assessing each horse's probability of winning.” INVESTOR CORROLARY: While we look at the move to passive as a negative; more passive money should increase the number of opportunities for active investors.

- “There is no shame in passing a race because you just don't see any value in it. Nor should you force yourself to play a race in which you have no confidence in your own odds line.” INVESTOR CORROLARY: Good ideas are hard to find, but worth the wait.

- “Recognize the difference between picking horses and making wagers in which you have an edge. The only path to consistent profit is to exploit the discrepancy between the true likelihood of an outcome and the odds being offered.” INVESTOR CORROLARY: Probability-weighted return is the arbiter of all decisions.

July 05, 2017

Jason Zweig on Being Your Own Quant

Until the day you are relieved of your cognitive biases, it is important to frequently remind yourself of your mental fallibility so that you can be on guard for their effects. Jason Zweig’s “How to Be Your Own Quant” is perfect for that task. Here are a few gems from the short article:

 - Take a hint from hedge-fund manager Magnetar Capital LLC, which is seeking to “take what was in our head and our database and make rules out of it” — measuring intuitions, testing them for reliability and then basing decisions on them. Human judgment is inconsistent. People are good at knowing what matters, but not very good at always looking at it the same way.

 - Presented with identical information under different circumstances, we come to different conclusions about it. The judgments of everyone from accountants to physicians and weather forecasters will vary depending on such factors as mood, time of day and how many other demands they have on their attention.

 - Decades ago, the psychologist Lewis Goldberg showed that if you determine which factors experts consider most important in coming to a conclusion, you can program a computer to size up a ​decision based on those — and only those — factors. The computer’s predictions using the experts’ criteria turned out to be more accurate than the experts’ own predictions, because the computer always interprets the same evidence the same way.

June 01, 2017

The Value of Price Targets

Abstract

We analyze the difference in ROIC (Return on Invested Capital) for the portion of client portfolios with price targets and the portion of client portfolios without price targets. In examining exposures, we find a long bias in the portion of the portfolio with research versus the portion of the portfolio without research making the Total measurements less meaningful. To neutralize this distortion, we look at differences on a long and short basis. We find that the portfolios with price targets outperform by 16% on an annualized ROIC basis (average of long and short improvement).

We also look at optimal position sizing suggested by the Alpha Theory algorithm and find that optimizing position sizes that have price targets adds an additional 5% for long positions.

Screen Shot 2017-06-01 at 2.32.50 PM

Table 1

Data

Alpha Theory tracks investment results for clients on a daily basis. We’re able to segment the daily returns for portfolios into two categories, securities with price targets and securities with no price targets. We’re then able to calculate an average daily return across our client base, segmenting into these two categories. All of our calculations are done on an ROIC basis for comparability purposes. Removing the exposure effect would result in returns of a portfolio that is 100% allocated to either category. For comparison, we also calculate returns based on optimal position sizing recommendations as well as returns on the ACWI, an all-world index.

Research

Figure 1 shows the cumulative returns over the time for the period of the analysis, where we break out client portfolios by the portion with and without price targets. We also look at how the optimal portfolio would have performed, which is a portfolio composed of securities with optimal position sizing output from Alpha Theory. Alpha Theory uses price target inputs to recommend position sizing based on probability-weighted return calculated from those price targets. Alpha Theory does not recommend position sizing on securities without research, so the comparison would be best made between optimal returns and the portion of the portfolio with price targets. We find that the portion of the portfolio with no price targets significantly underperforms the price target portfolio, by 11.5% on an annualized basis.  What we also see is that the price target portfolio, if sized optimally (where not already done so), would have increased performance even further, by 3.3%. Decomposing the exposures on the price target and non-price target portfolio reveals that the average net exposure for the price target portfolio is 40% and the average net exposure on the non-price target portfolio is -12%. This is an interesting divergence, as it tells us that managers are more likely to initiate a short position without research than a long position without research. With the non-price target portion running such a low net exposure, we would expect average returns to be roughly zero, as longs and shorts balance each other over a large sample. A clearer picture would be one where we break out the long and short portions.

Screen Shot 2017-06-01 at 2.39.03 PM

Figure 1

Figure 2 shows the same data for the long portion of the portfolio. We find that the long price target portfolio outperforms the long non-price target portfolio by 3.4% on an annualized basis. Sizing optimally (where not already done so) adds an additional 1.0% to the annualized return.

Screen Shot 2017-06-01 at 2.39.38 PM

Figure 2

We then look at the same data for the short portfolio in Figure 3. We find that the price target portfolio outperforms by 0.9%, on an annualized basis. Sizing optimally (where not already done so) would have added an additional 3.2%.

Screen Shot 2017-06-01 at 2.41.13 PM

Figure 3

Conclusion

Our assumption for why securities with price targets outperform those without is that price targets inform investors of value, make explicit the logic around their decisions, allow for optimal position sizing to be calculated and have a higher level of research rigor.

Investing in assets without first calculating price targets is deleterious to returns. This result is intuitive to most managers but hopefully this gives empirical evidence that will prevent future positions going into the portfolio without the critical step of defining risk-reward.

May 15, 2017

Changing The Course Of Active Management — The Concentration Manifesto

Is this the end of active portfolio management? You would think so if you listen to pundits. But I see it differently. I believe we have reached a critical juncture that will ultimately redefine the space for the better — where the winners will search for ways to constantly refine their process to maximize their edge.

At Alpha Theory, we are also searching for ways for our clients to maximize edge. To that end, about a year ago, while doing some research on the impact of “crowdedness” in portfolio sizing, my team and I discovered that crowded names consistently outperformed less crowded names. That made us wonder; in general, do holdings with bigger position sizes outperform those with a smaller position size? After digging through the numbers from a cross section of 60 funds totaling over $70 billion in assets under management, we found empirical evidence that they did.

We knew we were on to something. We then isolated our clients’ highest expected return positions to see if they were the best returns. They were. With all of this demonstrated skill and ability, the question remained: why do active managers underperform? The simple answer: low conviction positions negated most of the performance they generated with the high conviction names.

The Concentration Manifesto is my attempt to get a critical dialogue started between managers and allocators to ultimately improve the active management process. As you will see, the solution is simple, but not easy. It will require that both sides cast aside outdated thinking and embrace the notion that concentration is in their best interest. But by encouraging these important discussions, I believe we will be solidifying the long term survival of the active management industry.

I hope you find the analysis insightful and valuable and I look forward to being part of the conversation.

 

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April 17, 2017

Investor Bias Seen in Data

By Cameron Hight and Justin Harris

 

Alpha Theory’s Analytics Department studies clients’ historical data to provide useful insights. Over time, we have identified patterns that point to certain investor biases. Typically, biases are highlighted by deviations between actual and optimal position sizes. Said another way, biases occur when managers size positions different than what the risk-reward would suggest.

 

Here are a few examples:

 

1. NOT ADJUSTING POSITION SIZE AFTER A BIG PRICE MOVE: One of the most common biases we see in the data, is that after large positive price changes, managers are less likely to cut exposure, even though the probability-weighted return has diminished due to the move. The potential damage from this willful ignorance is compounded by a much larger position with a lower expected return. The typical behavior of investors is to let winners run, however, we’ve found that to be sub-optimal for fundamental funds.

The first step to alleviating this bias is to force re-underwriting names when they reach an unacceptable PWR. If the new assumptions justify the size, then all is good. If not, then the manager knows there is some bias that is causing them to stay in the position. Forcing re-underwriting at critical levels ensures that checks and balances are in place so that profits are kept and not lost on reversals.

 

2. NOT SIZING UP GOOD PROBABILITY-WEIGHTED RETURN WHEN INITIATING A POSITION: When analysts input price targets into Alpha Theory, and a manager decides to act on that information, what we’ve seen in the data is a tendency to build a position over time. We’ve found, on average, this is detrimental to returns. Slowly scaling into a high conviction and high probability weighted return name causes investors to miss some of the return potential.

 

3. UNDISCIPLINED APPROACH: Our data has shown that managers who are more disciplined (i.e. have more of their portfolio with price target coverage and size closer to optimal position sizing) tend to outperform those who don’t. Unfortunately, running complex sizing algorithms through our heads is not something we do well. What we see in the data is that positions without explicit price targets underperform. Be it hubris or any other number of reasons, it’s almost always detrimental to returns.

 

4. DIVERSIFYING: Our research shows that the largest positions in client portfolios outperform smaller names by a big margin, mostly because the batting average on top holdings is high. Most clients nullify this benefit by taking on many more names in the portfolio at much lower probability-weighted returns. We’ve done research which shows that concentrated portfolios outperform diversified portfolios by 2.2% on an alpha basis (run as a Monte Carlo study using batting averages calculated for various portions of client portfolios 2011-2016). The cost of diversification is a loss of alpha without a commensurate improvement in risk protection.

 

For 2016 returns, if clients sized using the suggested Optimal Position Size, they would have been better off by 5.1%. Clearly we recognize that not every position was able to be sized optimally, but even if half of that difference could have been captured, there was a lot of money left on the table. The biases above highlight why some of the difference occurs. It’s hard to beat an unemotional version of yourself, especially when we’re not psychologically built for the game.

March 13, 2017

Ted Seides - Alpha Theory Book Club

 

On March 7th, Alpha Theory hosted a book club with over 30 portfolio managers, analysts, and allocators coming together to discuss Ted Seides’ book, “So You Want to Start a Hedge Fund?”. We were lucky enough to have Ted present and answer questions about the capital raise environment, investment process best practices, hiring, keeping investors happy, etc.

 

Here are a few takeaways:

 

1. CAPITAL RAISE ENVIRONMENT: It’s hard out there and isn’t getting any easier. Allocators are getting pressure from their investors about their hedge fund investments.

2. INVESTING ENVIRONMENT: Once again, it’s hard out there and isn’t getting any easier. There are more smart managers than ever looking at the same ideas.

3. FEES: Fee pressure will continue and managers will be asked for fee strategies which better align the interests of the investor and the manager.

4. DURATION DISCONNECT: There has been, and probably always will be, a disconnect between the duration that a manager is judged and the duration in which a manager manages their portfolio. The best thing a manager can do is be open and honest about their challenges so that investors get comfortable with volatility of performance numbers.

5. TURNOVER: Managers should be quick to remove “bad fit” analysts, even if they’re going to get push-back from investors over changes with the team.

6. STASIS: Many hedge funds have a “set it and forget it” mentality towards culture, personnel, and investment process. Many great corporations have advanced human capital strategies and hedge funds can leverage that knowledge to build superior organizations (i.e. Bridgewater or Point72).

7. COACHES: To prevent stasis, it is important to read and sometimes bring in outside help. There are experts in team building, time management, bias mitigation, decision science, investment process, etc.

8. RUNNING A BUSINESS IS HARD: Most hedge fund managers don’t have the luxury of just picking stocks. They’re charged with hiring/firing, raising capital, investor relations, human resources, picking accountants, selecting offices, etc. All the things that a CEO of a company deals with plus managing a fund. The reason portfolio managers are so busy is because they have two full time jobs.

9. THE BET: As most know, Ted was the other side of the famous 10-year bet with Warren Buffett pitting the S&P 500 against a basket of hedge fund allocators. Ted still fully believes that hedge funds can outperform in the right environments (i.e. market is overbought).

 

Thanks to all those that attended and contact Alpha Theory if you would like to learn more about attending future book clubs.