The hours spent on planes and traipsing through airports for travel-weary executives fundraising for India’s new sovereign development fund, the National Investment and Infrastructure Fund (NIIF) are paying off.

Seeded with $3 billion by the Indian government in 2015, NIIF has so far crowded-in a further £1.3 billion to finance domestic infrastructure and growth across three funds. It includes commitments from a clutch of high-profile anchor investors which have signed up as majority owners in NIIF’s General Partner/Limited Partner model that compares to Australia’s IFM Investors, the investor-owned fund manager.

New names are increasingly knocking on the door of NIIF’s Delhi-headquarters as its reputation as the right partner for India gathers steam, enthuses Saloni Jhaveri, head of investor relationships, who moved home from the US to become one of the first members of staff on the scene in 2017.

“We have very credible local and international investors in our funds alongside the Indian government. We are also working with globally respected infrastructure operators,” she says.

NIIF was set up by the Indian government to help plug its infrastructure gap with long-term, stable equity capital. That meant shaking off the tag of bureaucracy, corruption and over leverage that had long blighted the sector, and putting in place a reliable local partner, able to catalyse government seed capital with best-in-class governance and market rate returns.

“We had to demonstrate to investors that they could make strong returns through investments in Indian infrastructure,” she recalls, declining to detail returns so far given it is still such early days.

Settling on an ownership model that placed the government at arms-length with a 49 per cent stake, and institutional capital centre stage with 51 per cent, NIFF went in search of anchor LPs.

Bagging four of India’s largest financial institutions (HDFC Group, ICICI Bank, Kotak Mahindra Life and Axis Bank) helped lend credibility and sustainability when chief executive Sujoy Bose began approaching bigger fish. NIIF subsequently netted Abu Dhabi Investment Authority – its first anchor – AustralianSuper, Singapore’s Temasek, Canada’s Ontario Teachers’ Pension Plan and, as of last December, Canada Pension Plan Investment Board (CPPIB) as founding LPs.

A process that was helped by what Jhaveri calls “generous” co-investment terms whereby each of NIIF’s international LPs – in contrast to the government and local partners – have 3:1 investment rights.

“If they put in $250 million they have a $750 million priority co-investment right,” she says.

Getting the governance right

But it took more than this to persuade LPs on board. NIIF’s governance structure played a key role in creating investor comfort, withstanding due diligence and stress testing to shift LPs response from friendly to serious. The structures and beliefs NIIF spent so long putting in place in the early years in a process that drew on wide expertise, including Stanford University’s Global Projects Centre, proved its worth, she says.

For example, NIIF’s governing board comprises NIIF’s chief executive Bose, a few independent directors, the LPs’ shareholder representatives and a representative from the Ministry of Finance. Each of NIIF’s three funds have an investment committee, but unlike the governing board, have no representatives from investors, peopled instead with experts from NIIF’s own 55-member investment team and independent members.

“Each of our funds has a different strategy and may have different investors. Hence the teams and investment committee members are unique to each fund. Each investment approval comes with an involved three stage process,” she says.

NIIF’s three funds comprise a Master Fund, a Fund of Funds and a Strategic Opportunities Fund, all focused on core infrastructure (particularly transport) and related sectors. The Master Fund currently has commitments of around $2.1 billion including the government’s commitment, and has achieved its target size, she says. The Fund of Funds is targeting a $1 billion close. It currently has $600 million in commitments and $200 in the pipeline.

“We need another $200 million to close out,” she says.

Elsewhere, the Strategic Fund marked its first investment with the acquisition of an infrastructure debt financing business to balance out the equity investment in the Master Fund.

Investment team

Jhaveri describes the investment culture as “battle scarred” and “from the private sector,” with much experience gleaned from the difficult years of over leverage and bank stress. It is a credibility that significantly contributes to investor comfort, she says.

“The feedback we get from our investors always highlights the strength of our team. They play a big part in drawing in investment,” she says.

And in keeping with the investor-owned model, NIIF’s team liaises and trades best practice with its LP partners where there is cross over, in a relationship where transparency is central. If “things come up” NIIF is quick to pick up the phone, she says.

“We say this is what we talked about; these are the things that are not working; these are the concerns.”

As the manager builds out its team and looks to attract its next wave of funding, hiring will increasingly focus on technical and operating skills, as well as ESG integration, she says.

“We will continue to strengthen our ESG capabilities and want to set the benchmark for ESG investment in India,” she says.

Role of the government

Elsewhere a policy team works closely with the government to maximise the partnership. Here NIIF’s focus is on analysing the projects the government sends its way, focusing on the commercial viability, exploring suitable structures and how best to monetise and make projects investable. There is no government pressure to invest if projects aren’t right. NIIFs investment team independently decides if projects are good enough to bid for, she says.

Local industry

India’s new sovereign development fund is also helping seed and nurture the country’s own investment sector.

The Fund of Funds portion allocates to India’s top quartile fund managers in investments focused on growth capital and diversification.

“Each investment that we have made through our Fund of Funds has been meaningful to our fund managers, including in some cases becoming substantial anchor investments for those funds,” she says.

With each fund having around 10-12 investments, NIIF’s capital is starting to flow down into hundreds of local companies. The manager also uses its larger capital investment to negotiate good terms, she says.

“There is still a lot to do. We’ve done a lot but there is still a lot to be done. India will be a major investment destination and we would like to be a major player in that opportunity,” she concludes.

Ask yourself a simple question: How old do you feel you are? Does the answer match your true age – or do you feel younger or do you feel older?

This isn’t a silly question. We have found that subjective age identity – that is how old we feel – shapes important economic behaviour such as our decision when to retire, how much we save and our risk appetite when it comes to investment. We argue that not only our true age but also our subjective age should be integrated into designing and marketing financial products and services like target date funds and pension products.

What is subjective age identity

Subjective age identity reflects a person’s subjective interpretation of his or her age – how old they feel they are. There are different reasons why one might feel younger or older than one’s true age.

We focus on two reasons in our research. First, ability: I might feel, for example, older, because my physical or cognitive abilities are worse than what they are supposed to be (or used to be).

Second, social construction and preferences: I might feel, for example, younger, because I am surrounded by young people in my day to day activities, do things that young people do like sport, or I might deny my true age because of real or perceived old age prejudices.

Existing studies have found that subjective age identity predicts many variables and behaviours such as physiological and cognitive functioning, dementia, performance at the workplace, and shopping preferences. To take an illustrative example, I might deny using the convenience of meals-on-wheels services because that is something that old people do, and I feel young.

Subjective age can be measured surprisingly simply – often it is just a single survey question that a person is being asked to answer: “Many people feel older or younger than they actually are. What age do you feel?”

How subjective age identity shapes economic behaviours

For our study we analysed survey responses from a representative U.S. household survey data set (the Health and Retirement Study) covering in multiple waves the years 2008 to 2014 (in total 9,284 respondents aged between 45 to 90 years). First we found that the majority of respondents (75 per cent) felt younger than their true age, 15 per cent felt no difference, and 10 per cent felt older. On average, respondents felt 10 years younger. Factors that contribute to feeling younger are, for example, optimism and life satisfaction, while health problems predict the opposite.

When we analysed the impact of this on economic decisions we found that people who feel young and who are still working expect to continue being employed in the years ahead rather than retiring; whilst those that are currently unemployed have a higher likelihood of returning to employment.

These effects are economically important: For example, feeling one standard deviation younger than the sample average (that is, 11 years) predicts a 1.1 per cent higher likelihood to be employed in a subsequent Health and Retirement Study wave. It seems small, but given the overall lower employment in the age bracket of our sample this number means a 21 per cent increase compared to the average likelihood.

We also found that respondents’ annual savings are impacted by their subjective age identity – although the effects are less straight forward. For those who feel younger because of higher physical or cognitive ability, we found an increase in savings while for those who feel younger because of social construction and preferences we found lower savings.

One interpretation of these findings is that those with a higher ability seem to plan for a longer life span, while those who want to mimic young behaviours to match their identity, engage in behaviours that cost money (think shopping and travelling). We found similar results when we looked at portfolio decisions. Subjective age matters for the share of assets invested riskily (for example in stocks), but again the reason for feeling younger predicts different adaptions of behaviour.

Implications for investment products and marketing

So, an answer to just a single question about one’s subjective age identity helps predict a wide range of economic preferences. How long people stay employed, when they might retire, how much they save and how they invest.

Our findings challenge the economic policies, financial advice, product design and marketing strategies that are based on chronological age. We think that subjective age should also be taken into account to provide products and services that match people’s preferences.

What makes subjective age identity particularly interesting is not only that it can be easily measured. It is also very stable across time. So, in the world of saving and investing where we need to make plans and decisions many years, if not decades, ahead, subjective age can be used to fine tune products and services.

What should be the target date of a target date fund, for example? We think that chronological age should provide a reference point from which services and products for individual clients are adjusted and tailored to match their subjective age identity. Likewise, a pension fund when defining the investment strategy for its portfolio might tailor the choice to match its specific member population’s subjective age identity. It could also help better predict decisions like when to retire, or if members will choose higher payouts for the first retirement years.

Dr Thomas Post is assistant professor of finance at Open University and Maastricht University. His research covers behavioural finance, household finance, and pension finance.

The latest Willis Towers Watson (WTW) Pension Risk Management Study gives a snapshot of the 2020 investment strategies of German pension funds, based on a survey of 38 participants with more than €163 billion of assets. We undertake the survey annually (our first study was in 2010) to provide insight into how the industry is evolving, drawing participants from a broad range of corporate and first pillar pension investors.

Around 60 per cent of these are unregulated, German corporates with a contractual trust arrangement. The remaining 40 per cent are quasi-insurance regulated, utilising differing funding vehicles (Pensionskasse, Pensionsfonds, First Pillar industry-wide funds) and therefore subject to more stringent regulations akin to those applied to insurers prior to the introduction of Solvency II.

On average, we found regulated investors allocate 65 per cent in bonds, 10 per cent to equities, 9 per cent to alternatives, 14 per cent to real estate with the remainder in cash. Unregulated investors have, instead, a somewhat reduced bond allocation, with some 44 per cent in bonds, 21 per cent in equities, 22 per cent in alternatives, 14 per cent in real estate and the residual in cash.

We found that on the whole returns have been acceptable, albeit dampened by low levels of equity investments. Some 71 per cent of the investors surveyed met their investment goals in the previous 12 months, although this was considerably lower than the corresponding result in 2018 of 96 per cent.

We note changes in the asset allocation of both regulated and unregulated investors over the last 10 years – indeed several stand-out investors having radically transformed their portfolios. For example, we’ve seen asset liability modelling approaches become standard practice in determining portfolios. Elsewhere, real estate portfolios, always a mainstay of German investors, have been substantially built out and emerging market allocations have also moved from marginal to core. We note the adoption of far more diversification in bonds, a reduction in the equity home bias and a doubling or even tripling of alternative allocations for regulated/unregulated investors. Also noteworthy is the increasing adoption of outsourcing (fiduciary management) arrangements to improve the governance and effectiveness of portfolios.

Challenges ahead

However, we would argue that despite that level of change, it has not kept pace with needs. Expected 10-year median returns for regulated investors are now 1.3 per cent versus unregulated investors forecast median returns of 2.2 per cent. Whereas unregulated (corporate) investors may be disappointed by this prognosis, they come in the context of their liabilities measured on a mark to market basis and with the potential for some relief should discount rates rise from their current low levels. Furthermore, corporates may well decide this level of return is acceptable within their broader corporate goals.

For regulated investors the situation is, however, more critical. They face a different asset/liability framework as they use fixed discount rates, typically set in the distant past with reference to long-term return expectations that range typically from 3-4 per cent. Only a few funds, sometimes at the insistence of the regulator, have adjusted their discount rates downwards (taking a one-off balance sheet hit as liabilities correspondingly rose) to accurately reflect less rosy returns.

Regulations stipulate that investors are always required to maintain funding levels above 100 per cent. This has led them to look to beat the discount rate year-on-year to build up reserves – or to eat into these reserves when returns are lower than discount rates. The expected future returns will, for many investors, lie below their required rates and thus reduce their reserves. The reserves for several regulated investors are already looking lean.

Risk budgets

Change is required, but many German investors are set in their ways. More investors need to reassess, and increase, their risk budgets. Risk budgets continue to be considerably lower than those of international peers, in reflection of an unnecessary degree of conservatism.

Among peers, German funds show the highest allocation to fixed income instruments and lowest allocations to risky assets, with equity levels particularly suppressed. Allocations to diversifying assets have increased in the last 10 years, but primarily in a one-to-one trade off with equity allocations. While this has improved the diversity of risk assets, it has done little for increasing overall return expectations.

Secondly, we believe portfolio construction could be significantly improved. By embracing a broader range of return drivers, the expected returns for regulated investors could be improved by nearly 50 per cent from 1.3 per cent to 2.1 per cent. For unregulated investors (able to spend more on risk and facing less regulatory constraints) the corresponding change is again approximately 50 per cent from 2.2 per cent to 3.4 per cent.

In particular, the BaFin regulations applied to regulated investors prescribe both the types of assets they can invest in, and the maximum amounts they can invest in the respective asset types. Furthermore, additional solvency requirements – broadly speaking a requirement to be 100 per cent funded at all times, based on the chosen long term fixed discount rate, with additional stress testing – lead to restrictions on risk. This has also “forced” regulated investors into a pro-cyclical reduction in risk, and an inability to “sit out” short-term market noise to profit from the long run.

Barriers to change

The complexity of Germany’s regulatory and tax environment is one barrier to change. The other barrier is one of governance. With increased portfolio diversity comes increased governance requirements. Whereas a small number of the larger corporate investors have addressed this by creating in-house teams, this is not a viable option for the bulk of corporate investors. Similarly, whereas larger regulated investors have built up dedicated resources, smaller and medium sized entities are challenged.

The low yield environment also highlights a key governance problem facing the regulated investors. A component of regulated investors investment strategy has always been to purchase long-dated, unlisted fixed income investments (“Schuldscheine”) and hold them to maturity. The regulatory framework has encouraged this approach by treating them as hold-to-maturity instruments with no balance sheet volatility. It’s just that yields are now substantially below what is required. Moreover, funds’ lean governance and portfolio construction and risk budget processes have not been able to keep pace with the need to continuously redesign and realign portfolios.

Our study suggests that in the last 10 years investors have increasingly looked to address governance challenges by outsourcing of selected asset classes. Study participants identified reasons for outsourcing as improved cost effectiveness and transparency, improved portfolio returns, a relief on the burden on internal resources, improved portfolio diversity, and improved risk management.

Short-term comfort vs long-term cost

Portfolios in Germany have evolved in the last 10 years and are gradually adapting to the challenging economic environment. Nevertheless, opportunities still exist to improve portfolio efficiency and the level of expected returns. This creates additional governance requirements, but solutions exist to address these, and increasingly we see evidence of these being adopted. The conservatism of the portfolios is seemingly more difficult to move. It reflects the higher degree of comfort required by German investors, but it is important to note that the short-term comfort this may provide may well come at a long-term cost.

Nigel Cresswell is head of investments, Germany, Willis Towers Watson.

Now that we’re in the midst of 2020, it might be easy for investors to forget how big a turnaround 2019 actually was for financial markets. One way to look at it is through the Aon Median Solvency Ratio, a quarterly survey that gauges the financial health of an important slice of the institutional investor community, Canadian defined benefit pension plans.

The ratio draws on a large database of plans to compare total assets to total pension liabilities on a solvency basis, while taking into account different legislative requirements across the country. And last year, the change in the median solvency ratio was remarkable – to put it mildly. It began 2019 at a low point – 95.3 per cent – suggesting that the median DB pension was less than fully funded. By the end of year, however, the ratio hit 102.3 per cent – a gain of seven percentage points that suggested the median DB pension plan was now in a surplus position.

That illustrates what investors already know: 2019 turned out to be a very good year in financial markets. The question now is whether markets – and, by extension, pension plan solvency – can stage a repeat performance in 2020. We have our doubts.

First, let’s take a closer look at the numbers. By Q4 2018, investor sentiment had soured, with what looked like good reason. The U.S. Federal Reserve had been raising rates; worries over slowing global economic growth were growing; trade tensions were heightening. By the end of the year, negative returns for major equity indexes, including the S&P/TSX composite (-8.9 per cent), MSCI World developed country (-0.6 per cent) and MSCI Emerging Markets (-6.9 per cent) indexes, were the norm. The S&P 500 (+4.1 per cent) provided one of the few, though hardly encouraging, exceptions.

But then, in 2019, sentiment turned. Central banks, led by the Fed, signalled that they were done raising rates (the Fed began cutting them in July), while trade tensions began to ease. Investors put their concerns over slowing growth on the backburner, and markets took off. Returns from the S&P/TSX, the S&P 500 and other developed country indices ended the year up over 20 per cent, in Canadian dollar terms; the MSCI Emerging Markets Index gained double digits.

That equity rally coincided with resurgent fixed income markets as bond yields fell: the FTSE Canada Long Term Bond Index gained 12.7 per cent, while the FTSE Canada Universe Bond Index of all-maturity bonds gained 6.9 per cent.

Those strong returns had positive implications for pension plan investment portfolios. Yet the fixed income rally had one downside for pension solvency: as bond prices rose, yields fell. The Government of Canada 10+ year bond yield declined by 37 basis points through the year. That yield, importantly, serves as the base rate for annuity purchase rates; when those go down, it puts upward pressure on defined-benefit pension plans’ liabilities, which correspondingly puts downward pressure on median solvency. Stellar asset returns last year helped pensions weather the impact of lower yields, but so did a technical change from the Canadian Institute of Actuaries, which published annuity purchase guidance that increased the spread over the base rate by 20 bps – effectively upping annuity purchase proxy rates. That provided a partial (one-time) offset against the effect of declining bond yields on the Median Solvency Ratio.

So what do we see from 2020? In short, we believe that this year is likely to be more challenging. Global markets are in a transition phase of higher uncertainty, in which the going looks likely to be harder for investors. Central to this gloomier outlook is the way we see the global economy shaping up this year and beyond – as well as monetary policymakers’ capacity to respond effectively.

It’s true that the headlines on trade, including a partial truce in the Sino-American conflict, have been encouraging so far this year. Yet trade tensions remain – and they remain at risk of growing. As well, global growth is still a concern, as much as some investors might want to wish it away. According to the International Monetary Fund, growth for 2019 is likely to come in at 2.9 per cent – the lowest mark since 2008-2009. The IMF sees an uptick in 2020, to 3.3 per cent, but that represents a downward revision to previous estimates.

Last year, as the risks from an escalation in the U.S-China trade conflict rose, we saw central banks go on a rate-cutting spree to stave off a sharp global downturn. In this way, they extended the already-long expansion phase of the business cycle. But would cuts be effective again? Going forward, we should be a bit wary of the argument that lower interest rates will produce much faster growth, for two reasons. First, interest rates are so low to start with that cuts might have less impact than if they were coming from higher levels. Second, there is the difficulty that in trying to extend what has already been the longest U.S. expansion in the last 150 years, it matters that the economy might not have much capacity left to grow without stress.

Equity markets barrelled through slowing economic growth (and lower corporate earnings growth) in 2019. In 2020, elevated valuations might significantly constrain the upside for risk assets. That will be especially so if underlying pressures on corporate profitability, which we began to see in 2019, continue this year. Pension plan solvency, in particular, could face the risk of a double-whammy. Lower yields from (ineffective) monetary easing could raise liabilities, while risk-asset returns might fail to provide a counterbalance if they fall victim to slowing economic growth.

Our view is that money can still be made this year, but we find it quite difficult to argue that 2019’s rebound can carry on unchecked. The further we look out, the harder it is to see anything other than seriously impaired return potential for markets in 2020.

Erwan Pirou is Canada CIO for Aon.

I chat with Vinesh, ceo at ExtractAlpha, on the research behind alternative data, stock selection techniques and the expertise required to differentiate signal from noise.

Nothing on this podcast is to be considered investment advice or a recommendation. No investment decision or activity should be undertaken without first seeking qualified and professional advice.

The pace of technological change and advances in machine learning and quantitative methods will result in a “shake out” in investment management according to Campbell Harvey, Professor of Finance at Duke University.

Harvey, who is well respected for his extensive research work on factors, says that even discretionary managers can not deal with the amount of data now available and need to use machine learning to help inform their decision making.

“The future of finance will be much more quantitative than it is today. We are moving much more in that direction whether it is systematic or discretionary trading, machine learning is here to stay,” he said in a podcast conversation with Michael Kollo [see below].

“However there’s a big spread in the competence in terms of applying it. These small firms running machine learning, will be defeated by firms which have been around for at least five years which have PhDs in mathematics, statistics, and machine learning and know the best way to process and do validation. They are the firms that will win.”

Harvey has developed a set of due diligence questions that investors should ask managers about the research process in terms of machine learning and big data.

“Right now it’s the wild west and people are playing the hype of machine learning. This is something I believe in, but there is a lot of snake oil out there.”

While Harvey acknowledges that in some scientific fields, such as genetic research, data mining is not necessarily a bad thing but in economics and finance it’s a completely different story.

“We have models from first principles that have had a significant impact especially in capital markets, and that allows us to layer on something else in terms of our process of discovery. There’s a paper floating around that looks at 2.4 million trading strategies based on balance sheet information of companies and correlates that with future stock returns. The results showed that the second-best strategy is a measure of EPS divided by rental payments the firm owes four years in the future. It makes no sense, but that variable knocks the ball out of the park. What I’m saying is simple: that variable pops up in the top 10 for a machine learning algorithm, but no one in their right mind would consider trading it. It is a false factor and would be discarded. The number one thing is we need to use the theory we have, and that guides us.”

Harvey also warns that machine learning is complicated and there are hundreds of different machine learning approaches so a potential problem needs to be matched with the right approach, with inference built in.

Factor research

Harvey is well-known for his prolific work on factors, including a paper that sought to collate all the factors that had been identified in academic finance – the result was more than 400 factors.

One of his substantial papers, co-authored with Wayne Ferson from the University of California was on the variation of risk premia.

“We made the economic case that risk premia change through time. If I wrote that paper today it would be called “Factor timing”,” he says.

Harvey now has some work in progress on matching risk with investment horizons.

“Some people have longer horizons than others and that creates some opportunities. There is a reason Warren Buffet is sitting on $150 billion of cash, he’s waiting for the recession… and he’s also long negative skew,” he says. “A lot of this depends on the horizon. I wish pension plans had these longer horizons. Buffett makes it happen even though he has shareholders, and pension funds seemingly have long horizons but they don’t act like it. They should be long illiquidity and welcoming a negative skew because they can ride out a GFC.”

Harvey, whose PhD showed that inverted yield curves predict recessions, was editor of the Journal of Finance for six years and he has published more than 125 scholarly articles. But despite his success as an academic he is critical of the motivations behind academic finance.

“The problem with finance research is it looks for an effect or factor that is significant statistically. The usual rule is 95 per cent confidence which is two standard deviations. However that only applies to a single test,” he says. “If you’re trying 20 different factors and one appeared to work and 19 didn’t, that’s no big deal, that’s what you would expect purely by luck, it’s got nothing to do with the factor being true…. Declaring a factor to be a real factor with a two standard deviation confidence is just false.”

[He previously received the Bernstein Fabozzi/Jacobs Levy Award for Best Article from the Journal of Portfolio Management for his research on distinguishing luck from skill.]

But this problem is not just about factor research, according to the Canadian, but extends to empirical research in finance. And much of it stems from the structures of academic finance as an industry and the importance of publication for academics.

“To get published in a top journal you have to have a good idea, and it’s almost always the case the result is significant. Journal editors are not enthusiastic about publishing non- results, because they don’t get cited and it doesn’t advance the journals profile,” he says.

In addition an academic in one of the top universities who is published in a top journal can look forward to a job for life, a reward system Harvey says leads to data mining.

“This leads to a massive data mining exercise where many things are tried until something works in the sample, you spin a story around it, and it gets published. Many years later it might be found not to hold up out of sample, but you’re already done because you already have tenure. Academically the incentive is to publish at whatever cost. It’s not like you’re falsifying or fabricating… but it is basically a data mined result.”

In terms of factor research, the same data sets have been used and analysed since the 1960s.

“The low hanging fruit has already been picked,” he says. “Because that is the case if you find something new and exciting I’m very sceptical. The barrier to find something new is very high.

“We need to be very careful about this. I have no problem with smart beta products based on risk premia we have known and have been tested for a long time. I do have a problem with ETFs wrapped around an academic paper published in 2015.”

According to Harvey a factor is a source of a risk premia that is excepted to persist in the long term. And this appears in two ways.

The first is structural, such as equities achieving higher returns than treasury bills, or illiquid assets returning more than liquid assets.

And the second is an alpha source that is purely a trading strategy.

“You find some information that is not incorporated in the market quickly and design a strategy. Once people figure it out, and use an algorithm to process the information faster, those are the types of alphas that will fade quickly. Investors need to distinguish between those two and time horizon is the defining thing.”

 

 

The Curious Quant series, hosted by Michael Kollo, is published by Conexus Financial, the publisher of conexust1f.flywheelstaging.com. It is a discussion between technically-minded professionals in the financial services, technology and data science fields that examines the application of new data and new methodologies to common problems in financial markets. The aim is to promote better discussions about these emerging areas, and a better understanding of new technologies.

Michael Kollo has a PhD in Finance is from the London School of Economics where he lectured in quantitative finance in addition to Imperial College and at the University of New South Wales. He has created models and led quantitative research teams at Blackrock, Fidelity and Axa Rosenberg in the UK before more recently moving to Australia where he established the quantitative team for the $50 billion industry superannuation fund, HESTA. 

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