As small-cap stocks rally for the first time in years, where should investors look for the best opportunities? Greg Tuorto, head of the US Small and SMID Cap team in Goldman Sachs Asset Management, discusses:

I'm going to start a thread where I casually post updates of my small-cap altcoin trades (each position under $300K) in the replies over time. Disclaimer: - This is not financial advice. I'm simply journaling my own trades. - I trade ultra low market cap coins with extreme volatility and uncertainty. - I usually risk only 0.1% to 0.5% of my net worth on these plays. I’m mentally prepared to lose it all. - I may enter and exit positions at any time without notice. If you follow, you risk becoming my exit liquidity. Yes, my EXIT LIQUIDITY🙂 我计划在这个推文下不断的通过回复更新我的小市值山寨币的交易之旅（低于30万美元的短期交易），但是我想提前声明： -我只是分享自己的交易思路，而不是让你跟随 - 我会交易高波动性和高不确定性的小市值币 - 我只拿我身价的0.1%到0.5%来做这些不确定的赌博类型的短期交易，我可以接受在一次交易里损失全部投入 - 我会在不提前通知的情况下进/出我的仓位，如果你跟随，你可能会被我浇给，成为我的退出流动性 - 提前声明了这么多，你自己跟了且亏损那就是你自己的问题。说人话：你会成为我的退出流动性，你会被我割，所以看看就行了，别跟单

Here is the full suite of trading ETFs made by @DirexionLETFs Daily Bull & Bear 3X ETFs $SOXL Daily Semiconductor Bull 3X ETF $SPXL Daily S&P 500 Bull 3X ETF $SOXS Daily Semiconductor Bear 3X ETF $TECL Daily Technology Bull 3X ETF $MIDU Daily Mid Cap Bull 3X ETF $SPXS Daily S&P 500 Bear 3X ETF $TNA Daily Small Cap Bull 3X ETF $TZA Daily Small Cap Bear 3X ETF $EDC Daily MSCI Emerging Markets Bull 3X ETF $EDZ Daily MSCI Emerging Markets Bear 3X ETF $EURL Daily FTSE Europe Bull 3X ETF $KORU Daily MSCI South Korea Bull 3X ETF $MEXX Daily MSCI Mexico Bull 3X ETF $YINN Daily FTSE China Bull 3X ETF $YANG Daily FTSE China Bear 3X ETF $TYD Daily 7-10 Year Treasury Bull 3X ETF $TYO Daily 7-10 Year Treasury Bear 3X ETF $TMF Daily 20+ Year Treasury Bull 3X ETF $TMV Daily 20+ Year Treasury Bear 3X ETF $CURE Daily Healthcare Bull 3X ETF $DFEN Daily Aerospace & Defense Bull 3X ETF $DPST Daily Regional Banks Bull 3X ETF $DRN Daily Real Estate Bull 3X ETF $DRV Daily Real Estate Bear 3X ETF $DUSL Daily Industrials Bull 3X ETF $FAS Daily Financial Bull 3X ETF $FAZ Daily Financial Bear 3X ETF $HIBL Daily S&P 500 High Beta Bull 3X ETF $HIBS Daily S&P 500 High Beta Bear 3X ETF $LABU Daily S&P Biotech Bull 3X ETF $LABD Daily S&P Biotech Bear 3X ETF $NAIL Daily Homebuilders & Supplies Bull 3X ETF $PILL Daily Pharmaceutical & Medical Bull 3X ETF $RETL Daily Retail Bull 3X ETF $TECS Daily Technology Bear 3X ETF $TPOR Daily Transportation Bull 3X ETF $UTSL Daily Utilities Bull 3X ETF $WANT Daily Consumer Discretionary Bull 3X ETF $WEBL Daily Dow Jones Internet Bull 3X ETF $WEBS Daily Dow Jones Internet Bear 3X ETF Daily Bull & Bear 2X ETFs $AIBU Daily AI and Big Data Bull 2X ETF $AIBD Daily AI and Big Data Bear 2X ETF $BRZU Daily MSCI Brazil Bull 2X ETF $CHAU Daily CSI 300 China A Share Bull 2X ETF $CWEB Daily CSI China Internet Index Bull 2X ETF $ERX Daily Energy Bull 2X ETF $ERY Daily Energy Bear 2X ETF $FNGG Daily NYSE FANG+ Bull 2X ETF $GUSH Daily S&P Oil & Gas Exp. & Prod. Bull 2X ETF $DRIP Daily S&P Oil & Gas Exp. & Prod. Bear 2X ETF INDL Daily MSCI India Bull 2X ETF JNUG Daily Junior Gold Miners Index Bull 2X ETF JDST Daily Junior Gold Miners Index Bear 2X ETF LMBO Daily Crypto Industry Bull 2X ETF NUGT Daily Gold Miners Index Bull 2X ETF DUST Daily Gold Miners Index Bear 2X ETF QQQU Daily Magnificent 7 Bull 2X ETF SPUU Daily S&P 500 Bull 2X ETF UBOT Daily Robotics, AI & Automation Index Bull 2X ETF URAA Daily Uranium Industry Bull 2X ETF Daily Bear 1X ETFs QQQD Daily Magnificent 7 Bear 1X ETF SPDN Daily S&P 500 Bear 1X ETF Single Stock L&I ETFs TSLL Daily TSLA Bull 2X ETF AAPU Daily AAPL Bull 2X ETF AAPD Daily AAPL Bear 1X ETF ADBU Daily ADBE Bull 2X ETF AMUU Daily AMD Bull 2X ETF AMDD Daily AMD Bear 1X ETF AMZU Daily AMZN Bull 2X ETF AMZD Daily AMZN Bear 1X ETF ASMU Daily ASML Bull 2X ETF AVL Daily AVGO Bull 2X ETF AVS Daily AVGO Bear 1X ETF BABU Daily BABA Bull 2X ETF BOEU Daily BA Bull 2X ETF BRKU Daily BRKB Bull 2X ETF CONX Daily COIN Bull 2X ETF CSCL Daily CSCO Bull 2X ETF CSCS Daily CSCO Bear 1X ETF ELIL Daily LLY Bull 2X ETF GGLL Daily GOOGL Bull 2X ETF GGLS Daily GOOGL Bear 1X ETF HODU Daily HOOD Bull 2X ETF LINT Daily INTC Bull 2X ETF LMTL Daily LMT Bull 2X ETF METU Daily META Bull 2X ETF METD Daily META Bear 1X ETF MRVU Daily MRVL Bull 2X ETF MSFU Daily MSFT Bull 2X ETF MSFD Daily MSFT Bear 1X ETF MUU Daily MU Bull 2X ETF MUD Daily MU Bear 1X ETF NFXL Daily NFLX Bull 2X ETF NFXS Daily NFLX Bear 1X ETF NVDU Daily NVDA Bull 2X ETF NVDD Daily NVDA Bear 1X ETF ORCU Daily ORCL Bull 2X ETF ORCS Daily ORCL Bear 1X ETF PLTU Daily PLTR Bull 2X ETF PLTD Daily PLTR Bear 1X ETF PALU Daily PANW Bull 2X ETF PALD Daily PANW Bear 1X ETF PYPU Daily PYPL Bull 2X ETF QCMU Daily QCOM Bull 2X ETF QCMD Daily QCOM Bear 1X ETF SHPU Daily SHOP Bull 2X ETF SOFA Daily SOFI Bull 2X ETF TSLS Daily TSLA Bear 1X ETF TSMX Daily TSM Bull 2X ETF TSMZ Daily TSM Bear 1X ETF TXNU Daily TXN Bull 2X ETF UNHU Daily UNH Bull 2X ETF XOMX Daily XOM Bull 2X ETF Titans L&I ETFs BIOT Daily Biotech Top 5 Bull 2X ETF TEXU Daily Energy Top 5 Bull 2X ETF TSXU Daily Semiconductors Top 5 Bull 2X ETF TSXD Daily Semiconductors Top 5 Bear 2X ETF TTXU Daily Technology Top 5 Bull 2X ETF TTXD Daily Technology Top 5 Bear 2X ETF Actively Managed Tactical ETFs HCMT HCM Tactical Enhanced US ETF Non-Leveraged ETFs COM Auspice Broad Commodity Strategy ETF QQQE NASDAQ-100 Equal Weighted Index ETF Disclosure: @DirexionINV is a WOLF Financial Partner

After thorough research, $FLNC looks extremely compelling at its $3 billion valuation post-earnings release. It’s rare to find a small-cap player in the U.S. energy sector that has secured two direct blockbuster enterprise client deals of this magnitude. The company’s backlog already stands above $5.6 billion, greatly de-risking its growth trajectory — and this does not include potential new mega-client pipelines from names like GOOGL and MSFT. These large-scale client partnerships are framework agreements, which are likely to convert into firm orders very soon in Q3, and are not yet reflected in the current backlog figures. Once these official orders are announced, they will act as a major bullish catalyst — much like the catalyst cycle for semiconductor stocks moving from qualification to mass production ramp-up. Citi analysts noted: “The potential for mega-scale client deals will likely overshadow all other factors this quarter. We expect formal announcements to trigger a positive market reaction.” I’ll make a bold call: once it announces not one, but two major enterprise client orders, the stock is poised for a material re-rating, which could happen anytime within the next three months. I’ve positioned for this near-term catalyst trade accordingly. Additionally, if the company expands its software segment to deliver around $288 million in net profit — driven by margin expansion on $6 billion revenue at a 13.0% gross margin — its forward P/E for 2027 would drop to roughly 11.6x. Despite its blue-chip client pipeline and massive order backlog de-risking growth, the stock has already pulled back roughly 50% from its February highs. In my view, this presents an excellent entry point.

🔡 Stocks ABC: M is for Market Cap Short for Market Capitalization. It’s the total value of all a company's shares. It tells you how big of a player the company really is. ❓ The Question: Do you prefer the safety of Big Tech giants or the potential of Small Cap "hidden gems"? 🏢

Here's the #1# thing most people don't know about Warren Buffett: There is nothing special about Buffett’s stock picking. That doesn’t mean that Buffett wasn’t a great investor. He was! Buffett was, by far, the greatest investor in history, by a huge margin. Over 486 months between October 1976 and March 2017 –— 41 years –— Berkshire Hathaway’s Class A stock earned an average excess return of 18.6% per year above U.S. Tbills. Annualized volatility was 23.5%. Sharpe ratio: 0.79. Berkshire’s Sharpe ratio of (0.79) is roughly 1.6x times the broad U.S. stock market’s Sharpe ratio of 0.49 over the same period. Among all large-cap U.S. stocks and mutual funds with 30-plus-year continuous track records, those are unmatched numbers. A dollar invested in Berkshire on October 31, 1976, was worth more than $3,685 by March 31, 2017. A dollar invested in the S&P 500 with dividends reinvested over the same period was worth approximately $76. Buffett beat a passive index by a multiple of 48. But he didn’t do it with stock picking! Three researchers at AQR Capital Management –— Andrea Frazzini, David Kabiller, and Lasse Heje Pedersen –— dissected Berkshire’s 50 years of investments through 2013. They expanded and republished their findings in 2018 in the Financial Analysts Journal, which is the most highly respected industry financial journal. Their work won the Graham and Dodd Award for the best published paper of the year. The paper is called Buffett’s Alpha. They found, after accounting for cheap leverage (from the insurance float) and exposure to a handful of publicly documented factor premiums, Buffett’s investment skill –— the portion of his returns that cannot be explained by any mechanical strategy –— is 0.3% per year. That's statistically indistinguishable from zero. In other words, the alpha that Berkshire enjoyed for 50 years (as it compounded capital at 24% a year!) wasn’t due to Buffett’s stock picking. So, how did he do it? He did it by gaining access to a huge amount of investment capital that he did not own, for free. Buffett’s track record was built on leverage. That’s a dirty word for most investors, but it's the secret behind Berkshire. The AQR researchers had access to something most Buffett commentators do not: 40 years of Berkshire’s audited financial statements and the full quarterly history of the public 13F stock portfolio. The researchers asked a specific question: If I take Berkshire’s monthly stock returns from October 1976 through March 2017, and I run a linear regression against a set of well-documented risk factors –— market beta, size, value, momentum, and two newer factors called Betting-Against-Beta and Quality-Minus-Junk (detailed below) –— how much of Buffett’s performance can the factors explain? And after the factors have been stripped out, how much excess return remains? The data show clearly there are a few qualities that drove Berkshire’s results. First, Buffett has always preferred large-cap stocks, contrary to the popular image of him as a small-cap value investor. He buys elephants. Second, no surprise, Buffett buys cheap. Berkshire is almost six standard deviations away from neutral on the value axis. So far the picture is ordinary. Every large- cap value manager in America loads positively on size and on value. Buffett’s genius lies in the last two factors. These last two factors are a little complicated, but please stick with me. There’s a new factor, that, like value and size, characterizes Buffett’s strategy. It’s called Betting-Against-Beta (“BAB”). What it means is intentionally investing in stocks with very low volatility. The BAB factor captures the excess return that accrues to investors who own low-beta stocks. Low-beta stocks have historically earned higher risk-adjusted returns than high-beta stocks. Financial theory teaches that higher beta (higher risk) should mean higher return. But it doesn’t. The opposite occurs, in fact. And Buffett was one of the very first people to figure this out. Why does this factor persist? In an efficient market, once that factor is known to investors, then they should bid the price up on low- beta stocks until it no longer provides an edge. The explanation, per the theory of AQR’s Frazzini and Pedersen’s theory, is that because ordinary investors do not use leverage and seek high returns, they create persistent excess demand for more volatile stocks. (Having worked with retail investors for 30 years, I can assure you that is true.) But, an investor with access to cheap leverage –— Warren Buffett, for instance –— can exploit the mispricing by owning the low-beta names and levering them up to produce market-beating returns. And the last factor that matters to Buffett is quality. Buffett buys companies with high returns on invested capital. Quality-Minus-Junk (“QMJ”) is a factor described by Cliff Asness, also at AQR with Frazzini, and Pedersen, in a 2019 paper in Review of Accounting Studies. The QMJ factor captures the return to owning stocks of high-quality companies –— profitable, growing, safe, with high payout ratios –— against stocks lacking those characteristics. QMJ has been positive and statistically significant in every major developed equity market for which it has been measured. Berkshire’s loading is 0.37, with a t-statistic of 4.6. –– meaning it is highly significant to Berkshire’s results. In plain English: Buffett only buys large, high- quality, low-volatility stocks of the highest quality. But, Berkshire’s results were not, in any way, unusual. Any investor buying these same kinds of stocks would have earned those same returns –– about 16% a year over time. So how did Berkshire compound at 23% a year? To figure that out, AQR’s researchers built a Berkshire replica. They constructed a simple, rules-based, publicly investable portfolio that mechanically tilts toward large-cap, cheap, low-beta, high-quality stocks, and levers it 1.6- to- 1 to match Berkshire’s insurance float leverage. The correlation between their replica’s returns and Berkshire’s were virtually identical. The authors’ conclusion is unambiguous. “In summary, we find that Buffett has developed a unique access to leverage that he has invested in safe, high-quality, cheap stocks and that these key characteristics can largely explain his impressive performance.” Berkshire’s cost of insurance float has averaged almost three percentage points below the Treasury bill rate across 50fifty years of data. In roughly two-thirds of all years, Berkshire has been paid to hold other people’s money. That is not an investment strategy. That is a financing miracle. It is also the living, breathing heart of Berkshire Hathaway. It’s what Buffett built, starting in 1967 when he paid $8.6 million for National Indemnity’s $19.4 million of float. And it is the factor every retail investor admiring Berkshire’s returns has never paid any attention to. The 1.6-to-1 leverage that AQR measured over the full period, financed at this negative cost, explains the dollar magnitude of Berkshire’s returns. How do we know? An unleveraged version of the same stock portfolio –— which you can approximate by looking at the 13F holdings alone –— has earned an average excess return of 12% percent per year. It’s Berkshire’s leverage that magnifies this excess return to 18.6 %percent. How does this square with Berkshire’s reported gains? Berkshire’s 18.6% excess return, plus the T-bill rate that averaged roughly 4.7% over 1976–2017, gives you a total nominal return of roughly 23% per year, which is the figure you usually see quoted for Berkshire’s historical performance. The 23% tells you what Berkshire returned. The 18.6% tells you how much of that return was compensation for taking investment risk, as opposed to the baseline yield every lender to the U.S. government was earning anyway. With both of Berkshire’s “edges” –— systematic factor exposures to cheap, high-quality, low-volatility stocks and roughly 1.6-to-1 leverage delivered with insurance float –— you get Berkshire Hathaway’s 23% annual gains over 60 years. It’s the structure that’s genius, not the stock picking. And that's very important because it means the original Berkshire formula can work for any investor. I show you exactly how, in my new book.